Campbell_2017_seurat_analysis
Matthew Banton
15/11/2018
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## cpm## set working directory
setwd("/media/data/mattb/projects/Brian_scRNAseq_website/R_projects/mouse_hypothalamus_scRNAseq")Analysis of Campbell scRNAseq data for Chow fed mice (5 replicates) using seurat
Load CHOW mouse data
## Load seurat object
seurat_campbell_chow<- readRDS(file = "./seurat_campbell_chow_just_created.rds")
seurat_campbell_chow## An object of class seurat in project CAMPBELL_CHOW
## 30000 genes across 11255 samples.Quality control on CHOW mouse data
## Import list of mouse mitocondrially encoded genes:
mito_genes<- read.csv("mito_genes_table.csv", header = TRUE)
## Make list of mitocondrial gene IDS:
mito_genes <- mito_genes$id_with_url
## Identify which mitochondrially expressed gene IDs are present in my dataset:
mito_genes_present<-seurat_campbell_chow@raw.data[mito_genes, ]
mito_genes_present<- mito_genes[!grepl("NA", rownames(mito_genes_present))]
seurat_campbell_chow@raw.data[mito_genes_present, 1:5]## SRR5164436_AAAAAATGCATG SRR5164436_AAAAACACGACG
## ENSMUSG00000000028 1 0
## ENSMUSG00000000037 0 0
## ENSMUSG00000000148 0 0
## ENSMUSG00000000142 0 0
## ENSMUSG00000000183 0 0
## ENSMUSG00000000149 0 0
## ENSMUSG00000000131 0 0
## ENSMUSG00000000125 0 0
## ENSMUSG00000000085 0 0
## ENSMUSG00000000171 0 0
## ENSMUSG00000000093 0 0
## ENSMUSG00000000157 0 0
## ENSMUSG00000000094 0 0
## ENSMUSG00000000127 0 0
## ENSMUSG00000000103 0 0
## ENSMUSG00000000182 0 0
## ENSMUSG00000000058 0 0
## ENSMUSG00000000154 0 0
## ENSMUSG00000000159 0 3
## ENSMUSG00000000056 0 0
## ENSMUSG00000000049 0 0
## ENSMUSG00000000168 0 0
## ENSMUSG00000000134 0 0
## ENSMUSG00000000078 0 0
## ENSMUSG00000000001 0 0
## ENSMUSG00000000194 0 0
## ENSMUSG00000000126 0 0
## ENSMUSG00000000167 0 0
## ENSMUSG00000000184 0 0
## ENSMUSG00000000088 2 0
## ENSMUSG00000000120 0 0
## ENSMUSG00000000031 0 0
## SRR5164436_AAAAAGAAAAAT SRR5164436_AAAAATGCACTA
## ENSMUSG00000000028 0 0
## ENSMUSG00000000037 0 0
## ENSMUSG00000000148 0 0
## ENSMUSG00000000142 0 0
## ENSMUSG00000000183 0 0
## ENSMUSG00000000149 0 0
## ENSMUSG00000000131 0 0
## ENSMUSG00000000125 0 0
## ENSMUSG00000000085 1 0
## ENSMUSG00000000171 0 3
## ENSMUSG00000000093 0 0
## ENSMUSG00000000157 0 0
## ENSMUSG00000000094 0 0
## ENSMUSG00000000127 0 0
## ENSMUSG00000000103 0 0
## ENSMUSG00000000182 0 0
## ENSMUSG00000000058 0 0
## ENSMUSG00000000154 0 0
## ENSMUSG00000000159 0 11
## ENSMUSG00000000056 0 0
## ENSMUSG00000000049 0 0
## ENSMUSG00000000168 0 0
## ENSMUSG00000000134 0 0
## ENSMUSG00000000078 3 0
## ENSMUSG00000000001 0 0
## ENSMUSG00000000194 0 0
## ENSMUSG00000000126 0 0
## ENSMUSG00000000167 0 0
## ENSMUSG00000000184 0 0
## ENSMUSG00000000088 0 2
## ENSMUSG00000000120 0 0
## ENSMUSG00000000031 0 0
## SRR5164436_AAAACACTTCAT
## ENSMUSG00000000028 0
## ENSMUSG00000000037 0
## ENSMUSG00000000148 0
## ENSMUSG00000000142 0
## ENSMUSG00000000183 0
## ENSMUSG00000000149 0
## ENSMUSG00000000131 2
## ENSMUSG00000000125 0
## ENSMUSG00000000085 1
## ENSMUSG00000000171 1
## ENSMUSG00000000093 0
## ENSMUSG00000000157 0
## ENSMUSG00000000094 0
## ENSMUSG00000000127 0
## ENSMUSG00000000103 0
## ENSMUSG00000000182 0
## ENSMUSG00000000058 0
## ENSMUSG00000000154 0
## ENSMUSG00000000159 14
## ENSMUSG00000000056 0
## ENSMUSG00000000049 0
## ENSMUSG00000000168 3
## ENSMUSG00000000134 0
## ENSMUSG00000000078 0
## ENSMUSG00000000001 0
## ENSMUSG00000000194 0
## ENSMUSG00000000126 0
## ENSMUSG00000000167 0
## ENSMUSG00000000184 0
## ENSMUSG00000000088 2
## ENSMUSG00000000120 0
## ENSMUSG00000000031 0dim(seurat_campbell_chow@raw.data[mito_genes_present, ])## [1] 32 11255## Calculate the percentage of mitcondrial gene counts per cell
percent_mito <- Matrix::colSums(seurat_campbell_chow@raw.data[mito_genes_present, ])/Matrix::colSums(seurat_campbell_chow@raw.data)
## Basic stats of proportion of mitocondrial gene expression per cell
summary(percent_mito)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0006089 0.0011086 0.0013382 0.0018298 0.0106698## add percentage mitocondrial genes into metadata
seurat_campbell_chow <- AddMetaData(object = seurat_campbell_chow,
metadata = percent_mito,
col.name = "percent_mito")
## Look at the seurat object meta data
head(seurat_campbell_chow@meta.data)## nGene nUMI orig.ident replicate_name percent_mito
## SRR5164436_AAAAAATGCATG 1158 2043 SRR5164436 SRR5164436 0.0014684288
## SRR5164436_AAAAACACGACG 2171 4531 SRR5164436 SRR5164436 0.0006623979
## SRR5164436_AAAAAGAAAAAT 1286 2154 SRR5164436 SRR5164436 0.0018578727
## SRR5164436_AAAAATGCACTA 3464 8755 SRR5164436 SRR5164436 0.0018289895
## SRR5164436_AAAACACTTCAT 3626 10143 SRR5164436 SRR5164436 0.0022691397
## SRR5164436_AAAACGAACATG 3775 10500 SRR5164436 SRR5164436 0.0012388031## QC plots of number of genes, UMIs, and % mitochondria
VlnPlot(object = seurat_campbell_chow,
features.plot = c("nGene", "nUMI", "percent_mito"),
nCol = 3,
x.lab.rot = TRUE,
point.size.use = 0.2
)
## QC plots to show the relationship between nUMIs and relative mitocondrial gene expression or number of genes.
par(mfrow = c(1, 2))
# GenePlot(object = seurat_campbell_chow, gene1 = "nUMI", gene2 = "percent_mito", do.hover = TRUE, pch.use = 1)
# GenePlot(object = seurat_campbell_chow, gene1 = "nUMI", gene2 = "nGene", do.hover = TRUE, pch.use = 1)
GenePlot(object = seurat_campbell_chow, gene1 = "nUMI", gene2 = "percent_mito", pch.use = 16, cex.use = 0.5)
GenePlot(object = seurat_campbell_chow, gene1 = "nUMI", gene2 = "nGene", pch.use = 16, cex.use = 0.5)
Filter cells after QC
Filter cells out with more than 0.4% of total gene expression comming from mitocondrially encoded genes and more than 6000 genes expressed.
## manual check; I already know all cells have >800 genes
table(seurat_campbell_chow@meta.data$percent_mito < 0.004 & seurat_campbell_chow@meta.data$nGene<6000)##
## FALSE TRUE
## 358 10897# FALSE TRUE
# 358 10897
## Filter cells with <0.4% percent_mito and <6000 genes
seurat_campbell_chow <- FilterCells(object = seurat_campbell_chow,
subset.names = c("nGene", "percent_mito"),
low.thresholds = c(800, -Inf),
high.thresholds = c(6000, 0.004))
seurat_campbell_chow## An object of class seurat in project CAMPBELL_CHOW
## 30000 genes across 10897 samples.# An object of class seurat in project CAMPBELL_CHOW
# 30000 genes across 10897 samples.
# 358 cells are filtered out; numbers consistent with aboveLog normalise gene expression per cell
## Plot graph of total expression before normalisation
hist(colSums(seurat_campbell_chow@data),
breaks = 100,
main = "Total expression before normalisation",
xlab = "Sum of expression")
## Normalise gene expression per cell
seurat_campbell_chow <- NormalizeData(object = seurat_campbell_chow, normalization.method = "LogNormalize",
scale.factor = 1e4)
## Plot graph of total expression after normalisation
hist(colSums(seurat_campbell_chow@data),
breaks = 100,
main = "Total expression after normalisation",
xlab = "Sum of expression")
Find genes whose expression varies between cells.
Find genes whose expression varies between cells, which will be used to construct principal componets between cells that will be used for clustering.
## Find variable genes by expression
seurat_campbell_chow <- FindVariableGenes(object = seurat_campbell_chow,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.05,
x.high.cutoff = 4,
y.cutoff = 0.75,
num.bin = 20,
binning.method = "equal_width"
)
# number of variable genes
length(seurat_campbell_chow@var.genes)## [1] 1817seurat_campbell_chow <- FindVariableGenes(object = seurat_campbell_chow, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1817 variable genesseurat_campbell_chow <- FindVariableGenes(object = seurat_campbell_chow, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 2346 variable genesseurat_campbell_chow <- FindVariableGenes(object = seurat_campbell_chow, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 4, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 4084 variable genesScale normalised gene expression per cell to remove unwanted sources of variation
Scale gene expression per cell by building linear models for nUMI, percent_mito and mouse replicate.
## Scale data nUMI, percent_mito and mouse replicate
seurat_campbell_chow <- ScaleData(object = seurat_campbell_chow, vars.to.regress = c("nUMI", "percent_mito", "replicate_name"))## Regressing out: nUMI, percent_mito, replicate_name##
## Time Elapsed: 1.362351167202 mins## Scaling data matrixPrincipal component anlysis of variable genes.
Principal component anlysis of variable genes for use in cell clustering.
## Perform principal component analysis on variable genes
seurat_campbell_chow <- RunPCA(object = seurat_campbell_chow,
pc.genes = seurat_campbell_chow@var.genes,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 5)## [1] "PC1"
## [1] "ENSMUSG00000025468" "ENSMUSG00000043388" "ENSMUSG00000027350"
## [4] "ENSMUSG00000044349" "ENSMUSG00000024261"
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## [1] "ENSMUSG00000002985" "ENSMUSG00000026385" "ENSMUSG00000031765"
## [4] "ENSMUSG00000018593" "ENSMUSG00000026701"
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## [1] "PC2"
## [1] "ENSMUSG00000067786" "ENSMUSG00000055254" "ENSMUSG00000031760"
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## [1] "ENSMUSG00000020077" "ENSMUSG00000036905" "ENSMUSG00000036896"
## [4] "ENSMUSG00000036887" "ENSMUSG00000060802"
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## [1] "PC3"
## [1] "ENSMUSG00000037625" "ENSMUSG00000031425" "ENSMUSG00000032854"
## [4] "ENSMUSG00000076439" "ENSMUSG00000036634"
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## [1] "ENSMUSG00000036896" "ENSMUSG00000036887" "ENSMUSG00000036905"
## [4] "ENSMUSG00000030579" "ENSMUSG00000058715"
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## [1] "PC4"
## [1] "ENSMUSG00000036896" "ENSMUSG00000036887" "ENSMUSG00000036905"
## [4] "ENSMUSG00000030579" "ENSMUSG00000058715"
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## [1] "ENSMUSG00000079018" "ENSMUSG00000022584" "ENSMUSG00000036256"
## [4] "ENSMUSG00000056492" "ENSMUSG00000075602"
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## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000022132" "ENSMUSG00000058897" "ENSMUSG00000033737"
## [4] "ENSMUSG00000007682" "ENSMUSG00000034810"
## [1] ""
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000038370"
## [4] "ENSMUSG00000027800" "ENSMUSG00000094800"
## [1] ""
## [1] ""## visualise top genes associated with principal components
VizPCA(object = seurat_campbell_chow, pcs.use = 1:9)
## Plot principal component 1 v's 2
PCAPlot(object = seurat_campbell_chow, dim.1 = 1, dim.2 = 2)
## Plot principal component 2 v's 3
PCAPlot(object = seurat_campbell_chow, dim.1 = 2, dim.2 = 3)
## Plot heat map for gene expression of principal component 1 genes
PCHeatmap(object = seurat_campbell_chow, pc.use = 1, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 2 genes
PCHeatmap(object = seurat_campbell_chow, pc.use = 2, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 3 genes
PCHeatmap(object = seurat_campbell_chow, pc.use = 3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 4 genes
PCHeatmap(object = seurat_campbell_chow, pc.use = 4, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot multiple heatmaps of gene expression per PC genes
PCHeatmap(object = seurat_campbell_chow,
pc.use = 5:18,
cells.use = 500,
do.balanced = TRUE,
label.columns = FALSE)
## Takes a pre-computed PCA (from most variable genes identified earlier) and projects this onto the entire dataset (all genes)
seurat_campbell_chow <- ProjectPCA(object = seurat_campbell_chow, do.print = TRUE)## [1] "PC1"
## [1] "ENSMUSG00000033061" "ENSMUSG00000047261" "ENSMUSG00000050711"
## [4] "ENSMUSG00000027581" "ENSMUSG00000055430" "ENSMUSG00000019986"
## [7] "ENSMUSG00000026576" "ENSMUSG00000021087" "ENSMUSG00000024268"
## [10] "ENSMUSG00000025468" "ENSMUSG00000043388" "ENSMUSG00000040785"
## [13] "ENSMUSG00000042750" "ENSMUSG00000027500" "ENSMUSG00000029223"
## [16] "ENSMUSG00000022577" "ENSMUSG00000036699" "ENSMUSG00000027350"
## [19] "ENSMUSG00000059361" "ENSMUSG00000019923" "ENSMUSG00000044349"
## [22] "ENSMUSG00000035964" "ENSMUSG00000027273" "ENSMUSG00000018965"
## [25] "ENSMUSG00000039278" "ENSMUSG00000071658" "ENSMUSG00000024261"
## [28] "ENSMUSG00000024423" "ENSMUSG00000000159" "ENSMUSG00000031840"
## [1] ""
## [1] "ENSMUSG00000002985" "ENSMUSG00000026385" "ENSMUSG00000031765"
## [4] "ENSMUSG00000018593" "ENSMUSG00000026701" "ENSMUSG00000000567"
## [7] "ENSMUSG00000050953" "ENSMUSG00000001025" "ENSMUSG00000031762"
## [10] "ENSMUSG00000035805" "ENSMUSG00000026728" "ENSMUSG00000067786"
## [13] "ENSMUSG00000095538" "ENSMUSG00000022528" "ENSMUSG00000058135"
## [16] "ENSMUSG00000005360" "ENSMUSG00000018102" "ENSMUSG00000027712"
## [19] "ENSMUSG00000029838" "ENSMUSG00000021250" "ENSMUSG00000030342"
## [22] "ENSMUSG00000032231" "ENSMUSG00000034467" "ENSMUSG00000053931"
## [25] "ENSMUSG00000008540" "ENSMUSG00000036570" "ENSMUSG00000026649"
## [28] "ENSMUSG00000017009" "ENSMUSG00000063564" "ENSMUSG00000059970"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000018451" "ENSMUSG00000021270" "ENSMUSG00000067786"
## [4] "ENSMUSG00000055254" "ENSMUSG00000026223" "ENSMUSG00000031428"
## [7] "ENSMUSG00000037926" "ENSMUSG00000052727" "ENSMUSG00000031760"
## [10] "ENSMUSG00000101111" "ENSMUSG00000037852" "ENSMUSG00000039278"
## [13] "ENSMUSG00000026701" "ENSMUSG00000031633" "ENSMUSG00000021379"
## [16] "ENSMUSG00000035805" "ENSMUSG00000034467" "ENSMUSG00000017390"
## [19] "ENSMUSG00000055430" "ENSMUSG00000025666" "ENSMUSG00000019986"
## [22] "ENSMUSG00000026649" "ENSMUSG00000095538" "ENSMUSG00000040785"
## [25] "ENSMUSG00000046432" "ENSMUSG00000079037" "ENSMUSG00000015222"
## [28] "ENSMUSG00000042750" "ENSMUSG00000000567" "ENSMUSG00000049154"
## [1] ""
## [1] "ENSMUSG00000020077" "ENSMUSG00000036905" "ENSMUSG00000036896"
## [4] "ENSMUSG00000036887" "ENSMUSG00000060802" "ENSMUSG00000079018"
## [7] "ENSMUSG00000030579" "ENSMUSG00000075602" "ENSMUSG00000028581"
## [10] "ENSMUSG00000029484" "ENSMUSG00000058715" "ENSMUSG00000026365"
## [13] "ENSMUSG00000024621" "ENSMUSG00000024397" "ENSMUSG00000036256"
## [16] "ENSMUSG00000029622" "ENSMUSG00000064373" "ENSMUSG00000021423"
## [19] "ENSMUSG00000022584" "ENSMUSG00000023992" "ENSMUSG00000015852"
## [22] "ENSMUSG00000056492" "ENSMUSG00000038642" "ENSMUSG00000001123"
## [25] "ENSMUSG00000040584" "ENSMUSG00000020154" "ENSMUSG00000016494"
## [28] "ENSMUSG00000046805" "ENSMUSG00000036353" "ENSMUSG00000041378"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000037625" "ENSMUSG00000031425" "ENSMUSG00000032854"
## [4] "ENSMUSG00000076439" "ENSMUSG00000036634" "ENSMUSG00000026830"
## [7] "ENSMUSG00000031775" "ENSMUSG00000006782" "ENSMUSG00000046160"
## [10] "ENSMUSG00000050121" "ENSMUSG00000032060" "ENSMUSG00000033579"
## [13] "ENSMUSG00000032517" "ENSMUSG00000022425" "ENSMUSG00000027375"
## [16] "ENSMUSG00000041607" "ENSMUSG00000020486" "ENSMUSG00000027562"
## [19] "ENSMUSG00000027858" "ENSMUSG00000040759" "ENSMUSG00000013523"
## [22] "ENSMUSG00000073680" "ENSMUSG00000022090" "ENSMUSG00000026888"
## [25] "ENSMUSG00000037166" "ENSMUSG00000027199" "ENSMUSG00000043448"
## [28] "ENSMUSG00000090996" "ENSMUSG00000011884" "ENSMUSG00000028412"
## [1] ""
## [1] "ENSMUSG00000027447" "ENSMUSG00000018451" "ENSMUSG00000036896"
## [4] "ENSMUSG00000036887" "ENSMUSG00000036905" "ENSMUSG00000030579"
## [7] "ENSMUSG00000058715" "ENSMUSG00000028581" "ENSMUSG00000024621"
## [10] "ENSMUSG00000022587" "ENSMUSG00000026576" "ENSMUSG00000024397"
## [13] "ENSMUSG00000023992" "ENSMUSG00000021423" "ENSMUSG00000015852"
## [16] "ENSMUSG00000038642" "ENSMUSG00000021268" "ENSMUSG00000008682"
## [19] "ENSMUSG00000046805" "ENSMUSG00000036353" "ENSMUSG00000050711"
## [22] "ENSMUSG00000033061" "ENSMUSG00000048163" "ENSMUSG00000060802"
## [25] "ENSMUSG00000040747" "ENSMUSG00000059498" "ENSMUSG00000030786"
## [28] "ENSMUSG00000050621" "ENSMUSG00000021665" "ENSMUSG00000036438"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000036896" "ENSMUSG00000036887" "ENSMUSG00000036905"
## [4] "ENSMUSG00000030579" "ENSMUSG00000058715" "ENSMUSG00000024621"
## [7] "ENSMUSG00000021423" "ENSMUSG00000028581" "ENSMUSG00000024397"
## [10] "ENSMUSG00000038642" "ENSMUSG00000023992" "ENSMUSG00000015852"
## [13] "ENSMUSG00000036353" "ENSMUSG00000046805" "ENSMUSG00000048163"
## [16] "ENSMUSG00000030786" "ENSMUSG00000059498" "ENSMUSG00000040747"
## [19] "ENSMUSG00000000682" "ENSMUSG00000021665" "ENSMUSG00000052160"
## [22] "ENSMUSG00000040229" "ENSMUSG00000036908" "ENSMUSG00000018008"
## [25] "ENSMUSG00000030844" "ENSMUSG00000069516" "ENSMUSG00000044811"
## [28] "ENSMUSG00000020377" "ENSMUSG00000089929" "ENSMUSG00000052336"
## [1] ""
## [1] "ENSMUSG00000079018" "ENSMUSG00000022584" "ENSMUSG00000036256"
## [4] "ENSMUSG00000056492" "ENSMUSG00000075602" "ENSMUSG00000040584"
## [7] "ENSMUSG00000020154" "ENSMUSG00000041378" "ENSMUSG00000030237"
## [10] "ENSMUSG00000030235" "ENSMUSG00000061353" "ENSMUSG00000029648"
## [13] "ENSMUSG00000039167" "ENSMUSG00000001946" "ENSMUSG00000033960"
## [16] "ENSMUSG00000031239" "ENSMUSG00000039349" "ENSMUSG00000024140"
## [19] "ENSMUSG00000006386" "ENSMUSG00000020717" "ENSMUSG00000114487"
## [22] "ENSMUSG00000042116" "ENSMUSG00000031871" "ENSMUSG00000027435"
## [25] "ENSMUSG00000019966" "ENSMUSG00000034738" "ENSMUSG00000062960"
## [28] "ENSMUSG00000040732" "ENSMUSG00000039831" "ENSMUSG00000045954"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000022132" "ENSMUSG00000058897" "ENSMUSG00000033737"
## [4] "ENSMUSG00000007682" "ENSMUSG00000034810" "ENSMUSG00000029838"
## [7] "ENSMUSG00000022528" "ENSMUSG00000055653" "ENSMUSG00000063564"
## [10] "ENSMUSG00000045005" "ENSMUSG00000030629" "ENSMUSG00000052387"
## [13] "ENSMUSG00000022816" "ENSMUSG00000026185" "ENSMUSG00000047786"
## [16] "ENSMUSG00000021250" "ENSMUSG00000017493" "ENSMUSG00000017390"
## [19] "ENSMUSG00000024518" "ENSMUSG00000064351" "ENSMUSG00000022419"
## [22] "ENSMUSG00000034640" "ENSMUSG00000008540" "ENSMUSG00000030111"
## [25] "ENSMUSG00000064370" "ENSMUSG00000093460" "ENSMUSG00000038418"
## [28] "ENSMUSG00000061718" "ENSMUSG00000027239" "ENSMUSG00000064215"
## [1] ""
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000038370"
## [4] "ENSMUSG00000027800" "ENSMUSG00000094800" "ENSMUSG00000044772"
## [7] "ENSMUSG00000027744" "ENSMUSG00000072674" "ENSMUSG00000032595"
## [10] "ENSMUSG00000110332" "ENSMUSG00000045655" "ENSMUSG00000041323"
## [13] "ENSMUSG00000047139" "ENSMUSG00000095304" "ENSMUSG00000108841"
## [16] "ENSMUSG00000022037" "ENSMUSG00000047394" "ENSMUSG00000029182"
## [19] "ENSMUSG00000072473" "ENSMUSG00000046242" "ENSMUSG00000047671"
## [22] "ENSMUSG00000044475" "ENSMUSG00000020473" "ENSMUSG00000027360"
## [25] "ENSMUSG00000026683" "ENSMUSG00000028441" "ENSMUSG00000038135"
## [28] "ENSMUSG00000026301" "ENSMUSG00000091345" "ENSMUSG00000027867"
## [1] ""
## [1] ""Determine statistically significant principal components
## Perform jackstraw statistical test to investigate statistically significant PC.
seurat_campbell_chow <- JackStraw(object = seurat_campbell_chow,
num.replicate = 100,
display.progress = TRUE
)##
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## Time Elapsed: 7.0821281393369 mins# Maximum number of PCs allowed = 20.
## Visualise JackStraw plots
JackStrawPlot(object = seurat_campbell_chow, PCs = 1:20)## Warning: Removed 25440 rows containing missing values (geom_point).
## An object of class seurat in project CAMPBELL_CHOW
## 30000 genes across 10897 samples.## A less computationally intensive heuristic method for finding the statistically significant PCAs is using an elbow plot
PCElbowPlot(object = seurat_campbell_chow)
There is little difference in the Jackstraw and elbow plot when using 1817, 2346 or 4084 variable genes.
Cell clustering
## Cluster cells by PC
seurat_campbell_chow <- FindClusters(object = seurat_campbell_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_chow)## Parameters used in latest FindClusters calculation run on: 2018-11-20 17:35:29
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_chow <- RunTSNE(object = seurat_campbell_chow, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_chow)
TSNEPlot(object = seurat_campbell_chow,
no.legend = TRUE,
do.label = TRUE)
## 4084 variable genes = 19 clusters
## 2346 variable genes = 20 clusters
## 1817 variable genes = 20 clusters
table(seurat_campbell_chow@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 2262 1772 1405 1338 653 536 527 343 324 304 250 209 207 188 165
## 15 16 17 18 19
## 136 110 66 58 44proportion_table<- table(seurat_campbell_chow@ident, seurat_campbell_chow@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164436 SRR5164437 SRR5164438 SRR5164443 SRR5164445
## 0 0.10912 0.37939 0.07658 0.42257 0.31911
## 1 0.16581 0.12646 0.23146 0.11024 0.08535
## 2 0.11880 0.14857 0.12349 0.17585 0.10281
## 3 0.17037 0.04129 0.18938 0.03150 0.05044
## 4 0.07123 0.03609 0.07347 0.03675 0.06305
## 5 0.04501 0.05299 0.03174 0.01837 0.11251
## 6 0.04416 0.04681 0.05864 0.03150 0.04462
## 7 0.04074 0.02341 0.03794 0.00787 0.01455
## 8 0.02764 0.03023 0.02898 0.03937 0.03395
## 9 0.04359 0.01528 0.03277 0.01050 0.00485
## 10 0.02707 0.02438 0.02346 0.00787 0.00873
## 11 0.03020 0.00748 0.02518 0.00262 0.00582
## 12 0.02194 0.01983 0.01035 0.02625 0.02813
## 13 0.02165 0.01073 0.01587 0.01575 0.02619
## 14 0.01880 0.01040 0.01069 0.02100 0.02716
## 15 0.01396 0.01300 0.01414 0.01575 0.00000
## 16 0.00769 0.00293 0.00000 0.02362 0.06305
## 17 0.00969 0.00455 0.00414 0.00262 0.00485
## 18 0.00627 0.00488 0.00586 0.00000 0.00388
## 19 0.00627 0.00130 0.00586 0.00000 0.00097TSNEPlot(object = seurat_campbell_chow,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = 5))
colnames(proportion_table)<- colnames(table(seurat_campbell_chow@ident,
seurat_campbell_chow@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_chow <- FindClusters(object = seurat_campbell_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_chow)## Parameters used in latest FindClusters calculation run on: 2018-11-20 17:35:29
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_chow <- RunTSNE(object = seurat_campbell_chow, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_chow)
TSNEPlot(object = seurat_campbell_chow,
no.legend = TRUE,
do.label = TRUE)
## 4084 variable genes = 21 clusters
## 2346 variable genes = 22 clusters
table(seurat_campbell_chow@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1851 1685 1406 1255 660 631 542 378 343 326 304 285 250 209 188
## 15 16 17 18 19 20
## 165 136 110 70 58 45proportion_table<- table(seurat_campbell_chow@ident, seurat_campbell_chow@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164436 SRR5164437 SRR5164438 SRR5164443 SRR5164445
## 0 0.08832 0.31209 0.05795 0.32808 0.27934
## 1 0.15954 0.11834 0.22146 0.09449 0.08050
## 2 0.11880 0.14857 0.12384 0.17585 0.10281
## 3 0.15698 0.04454 0.17040 0.03937 0.05626
## 4 0.07322 0.03576 0.07382 0.03675 0.06305
## 5 0.04872 0.06860 0.06175 0.03937 0.05335
## 6 0.04501 0.05299 0.03380 0.02100 0.11154
## 7 0.02991 0.04194 0.03036 0.07612 0.02619
## 8 0.04074 0.02341 0.03794 0.00787 0.01455
## 9 0.02735 0.03056 0.02967 0.03937 0.03395
## 10 0.04359 0.01528 0.03277 0.01050 0.00485
## 11 0.02621 0.02828 0.01932 0.04199 0.03298
## 12 0.02707 0.02438 0.02346 0.00787 0.00873
## 13 0.03020 0.00748 0.02518 0.00262 0.00582
## 14 0.02165 0.01073 0.01587 0.01575 0.02619
## 15 0.01880 0.01040 0.01069 0.02100 0.02716
## 16 0.01396 0.01300 0.01414 0.01575 0.00000
## 17 0.00769 0.00293 0.00000 0.02362 0.06305
## 18 0.00969 0.00455 0.00552 0.00262 0.00485
## 19 0.00627 0.00488 0.00586 0.00000 0.00388
## 20 0.00627 0.00130 0.00621 0.00000 0.00097TSNEPlot(object = seurat_campbell_chow,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = 5))
colnames(proportion_table)<- colnames(table(seurat_campbell_chow@ident,
seurat_campbell_chow@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_chow <- FindClusters(object = seurat_campbell_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 1.5, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_chow)## Parameters used in latest FindClusters calculation run on: 2018-11-20 17:35:29
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_chow <- RunTSNE(object = seurat_campbell_chow, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_chow)
TSNEPlot(object = seurat_campbell_chow,
no.legend = TRUE,
do.label = TRUE)
## 4084 variable genes = 25 clusters
## 2346 variable genes = 24 clusters
table(seurat_campbell_chow@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1603 1257 1061 764 712 671 651 641 581 546 343 320 304 251 209
## 15 16 17 18 19 20 21 22
## 207 188 165 136 111 74 58 44proportion_table<- table(seurat_campbell_chow@ident, seurat_campbell_chow@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164436 SRR5164437 SRR5164438 SRR5164443 SRR5164445
## 0 0.07179 0.27926 0.04277 0.29134 0.24927
## 1 0.15641 0.04616 0.17075 0.03675 0.05529
## 2 0.10969 0.06469 0.14108 0.04987 0.04753
## 3 0.04046 0.11541 0.03587 0.16010 0.09893
## 4 0.05584 0.06177 0.09107 0.06037 0.03783
## 5 0.04957 0.08355 0.05002 0.10761 0.05238
## 6 0.07151 0.03609 0.07244 0.03675 0.06305
## 7 0.07806 0.03316 0.08762 0.01575 0.00485
## 8 0.04615 0.06047 0.03242 0.03675 0.12124
## 9 0.04501 0.05104 0.05933 0.03150 0.04559
## 10 0.04074 0.02341 0.03794 0.00787 0.01455
## 11 0.02735 0.02958 0.02863 0.03937 0.03395
## 12 0.04359 0.01528 0.03277 0.01050 0.00485
## 13 0.02735 0.02438 0.02346 0.00787 0.00873
## 14 0.03020 0.00748 0.02518 0.00262 0.00582
## 15 0.02194 0.01983 0.01035 0.02625 0.02813
## 16 0.02165 0.01073 0.01587 0.01575 0.02619
## 17 0.01880 0.01040 0.01069 0.02100 0.02716
## 18 0.01396 0.01300 0.01414 0.01575 0.00000
## 19 0.00769 0.00293 0.00000 0.02362 0.06402
## 20 0.00969 0.00520 0.00586 0.00262 0.00582
## 21 0.00627 0.00488 0.00586 0.00000 0.00388
## 22 0.00627 0.00130 0.00586 0.00000 0.00097TSNEPlot(object = seurat_campbell_chow,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = 5))
colnames(proportion_table)<- colnames(table(seurat_campbell_chow@ident,
seurat_campbell_chow@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_chow <- FindClusters(object = seurat_campbell_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 2.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_chow)## Parameters used in latest FindClusters calculation run on: 2018-11-20 17:35:29
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_chow <- RunTSNE(object = seurat_campbell_chow, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_chow)
TSNEPlot(object = seurat_campbell_chow,
no.legend = TRUE,
do.label = TRUE)
## 4084 variable genes = 29 clusters
## 2346 variable genes = 28 clusters
table(seurat_campbell_chow@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 959 745 736 674 658 655 618 614 585 572 476 474 436 343 320 304 289 251
## 18 19 20 21 22 23 24 25 26 27
## 209 207 188 136 110 109 70 58 56 45proportion_table<- table(seurat_campbell_chow@ident, seurat_campbell_chow@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164436 SRR5164437 SRR5164438 SRR5164443 SRR5164445
## 0 0.03419 0.18108 0.02139 0.19423 0.14161
## 1 0.03875 0.11248 0.03484 0.16010 0.09796
## 2 0.08917 0.04486 0.07796 0.03675 0.04365
## 3 0.05185 0.06014 0.08486 0.06037 0.03686
## 4 0.07977 0.03544 0.08831 0.01575 0.00679
## 5 0.06496 0.05104 0.07278 0.04199 0.04171
## 6 0.06781 0.03316 0.06968 0.03150 0.06208
## 7 0.07977 0.00065 0.11142 0.00000 0.00873
## 8 0.03618 0.08778 0.03139 0.11286 0.05238
## 9 0.04587 0.05917 0.03174 0.03412 0.12027
## 10 0.05242 0.01821 0.07692 0.01312 0.00776
## 11 0.02792 0.07217 0.01690 0.08136 0.07177
## 12 0.03162 0.04649 0.03794 0.03412 0.05723
## 13 0.04074 0.02341 0.03794 0.00787 0.01455
## 14 0.02735 0.02958 0.02863 0.03937 0.03395
## 15 0.04359 0.01528 0.03277 0.01050 0.00485
## 16 0.02422 0.02958 0.02725 0.01050 0.02910
## 17 0.02735 0.02438 0.02346 0.00787 0.00873
## 18 0.03020 0.00748 0.02518 0.00262 0.00582
## 19 0.02194 0.01983 0.01035 0.02625 0.02813
## 20 0.02165 0.01073 0.01587 0.01575 0.02619
## 21 0.01396 0.01300 0.01414 0.01575 0.00000
## 22 0.00769 0.00293 0.00000 0.02362 0.06305
## 23 0.01026 0.00943 0.00586 0.01312 0.02134
## 24 0.00969 0.00455 0.00552 0.00262 0.00485
## 25 0.00627 0.00488 0.00586 0.00000 0.00388
## 26 0.00855 0.00098 0.00483 0.00787 0.00582
## 27 0.00627 0.00130 0.00621 0.00000 0.00097TSNEPlot(object = seurat_campbell_chow,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4 )
# install.packages("PerformanceAnalytics")
proportion_table<- data.frame(matrix(proportion_table, ncol = 5))
colnames(proportion_table)<- colnames(table(seurat_campbell_chow@ident,
seurat_campbell_chow@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
# cor(proportion_table[1],proportion_table[4])
## Cluster cells using final parameters (1817 genes, 20 PC, resolution = 0.6)
seurat_campbell_chow <- FindClusters(object = seurat_campbell_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
## Produce t-SNE
seurat_campbell_chow <- RunTSNE(object = seurat_campbell_chow, dims.use = 1:20, do.fast = TRUE)Use 1817 genes, 20 principal components and a resolution of 0.6 to give 20 individual clusters. Less clusters are more likely to give more meaningfull functionally distinct groups of cells.
Finding differentially expressed genes between cell clusters (cluster biomarkers)
## Find markers for every cluster compared to all remaining cells, report both positive and negative genes.
seurat_campbell_chow_biomarkers <- FindAllMarkers(object = seurat_campbell_chow, only.pos = FALSE, min.pct = 0.2)
## Get the top 10 biomarkers per cluster
top10_seurat_campbell_markers<- seurat_campbell_chow_biomarkers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers## # A tibble: 200 x 7
## # Groups: cluster [20]
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0. 0.823 0.94 0.53 0. 0 ENSMUSG00000047261
## 2 0. 0.772 0.859 0.445 0. 0 ENSMUSG00000027500
## 3 0. 0.716 0.982 0.701 0. 0 ENSMUSG00000055430
## 4 9.02e-250 0.740 0.822 0.445 2.71e-245 0 ENSMUSG00000044349
## 5 1.18e-248 0.957 0.757 0.432 3.54e-244 0 ENSMUSG00000060188
## 6 9.45e-220 0.723 0.935 0.717 2.83e-215 0 ENSMUSG00000021268
## 7 2.78e-202 0.689 0.734 0.407 8.35e-198 0 ENSMUSG00000000159
## 8 4.90e-159 0.645 0.561 0.269 1.47e-154 0 ENSMUSG00000066392
## 9 6.27e-139 1.46 0.382 0.156 1.88e-134 0 ENSMUSG00000021647
## 10 1.95e- 79 1.92 0.271 0.122 5.84e- 75 0 ENSMUSG00000020660
## # ... with 190 more rows# write.csv(as.data.frame(seurat_campbell_chow_biomarkers), file = "seurat_campbell_chow_biomarkers.csv", quote = FALSE)
# write.csv(as.data.frame(top10_seurat_campbell_markers), file = "top10_seurat_campbell_chow_biomarkers.csv", quote = FALSE)
## Perform ROC DE test. This can take a long time.
seurat_campbell_chow_biomarkers_ROC <- FindAllMarkers(object = seurat_campbell_chow, only.pos = FALSE, min.pct = 0.2, test.use = "roc")
top10_seurat_campbell_markers_ROC<- seurat_campbell_chow_biomarkers_ROC %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers_ROC## # A tibble: 200 x 9
## # Groups: cluster [20]
## myAUC avg_diff power avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0.783 0.823 0.566 0.823 0.94 0.53 NA 0 ENSMUSG00…
## 2 0.78 0.716 0.56 0.716 0.982 0.701 NA 0 ENSMUSG00…
## 3 0.755 0.614 0.51 0.614 0.987 0.697 NA 0 ENSMUSG00…
## 4 0.754 0.772 0.508 0.772 0.859 0.445 NA 0 ENSMUSG00…
## 5 0.717 0.740 0.434 0.740 0.822 0.445 NA 0 ENSMUSG00…
## 6 0.715 0.957 0.430 0.957 0.757 0.432 NA 0 ENSMUSG00…
## 7 0.714 0.723 0.428 0.723 0.935 0.717 NA 0 ENSMUSG00…
## 8 0.711 0.634 0.422 0.634 0.803 0.419 NA 0 ENSMUSG00…
## 9 0.705 0.644 0.410 0.644 0.763 0.403 NA 0 ENSMUSG00…
## 10 0.701 0.623 0.402 0.623 0.771 0.399 NA 0 ENSMUSG00…
## # ... with 190 more rows# write.csv(as.data.frame(seurat_campbell_chow_biomarkers_ROC), file = "seurat_campbell_chow_biomarkers_ROC.csv", quote = FALSE)
# write.csv(as.data.frame(top10_seurat_campbell_markers_ROC), file = "top10_seurat_campbell_chow_biomarkers_ROC.csv", quote = FALSE)
## Plot heatmap of top 10 DE genes
DoHeatmap(object = seurat_campbell_chow,
genes.use = top10_seurat_campbell_markers$gene,
slim.col.label = TRUE,
remove.key = TRUE)
Save seurat object of chow mice.
## save seurat object as .rds
#saveRDS(seurat_campbell_chow, file = "./seurat_campbell_chow_final.rds")EdgeR batch correction of CHOW fed mice
Analysis of Campbell scRNAseq data for Chow fed mice (5 replicates) using seurat with batch correction
Load CHOW mouse data
## Load seurat object
seurat_campbell_batch_edgeR_chow<- readRDS(file = "./seurat_campbell_chow_just_created.rds")
seurat_campbell_batch_edgeR_chow## An object of class seurat in project CAMPBELL_CHOW
## 30000 genes across 11255 samples.# An object of class seurat in project CAMPBELL_CHOW
# 30000 genes across 11255 samples.Quality control on CHOW mouse data
## Import list of mouse mitocondrially encoded genes:
mito_genes<- read.csv("mito_genes_table.csv", header = TRUE)
## Make list of mitocondrial gene IDS:
mito_genes <- mito_genes$id_with_url
## Identify which mitochondrially expressed gene IDs are present in my dataset:
mito_genes_present<-seurat_campbell_batch_edgeR_chow@raw.data[mito_genes, ]
mito_genes_present<- mito_genes[!grepl("NA", rownames(mito_genes_present))]
seurat_campbell_batch_edgeR_chow@raw.data[mito_genes_present, 1:5]## SRR5164436_AAAAAATGCATG SRR5164436_AAAAACACGACG
## ENSMUSG00000000028 1 0
## ENSMUSG00000000037 0 0
## ENSMUSG00000000148 0 0
## ENSMUSG00000000142 0 0
## ENSMUSG00000000183 0 0
## ENSMUSG00000000149 0 0
## ENSMUSG00000000131 0 0
## ENSMUSG00000000125 0 0
## ENSMUSG00000000085 0 0
## ENSMUSG00000000171 0 0
## ENSMUSG00000000093 0 0
## ENSMUSG00000000157 0 0
## ENSMUSG00000000094 0 0
## ENSMUSG00000000127 0 0
## ENSMUSG00000000103 0 0
## ENSMUSG00000000182 0 0
## ENSMUSG00000000058 0 0
## ENSMUSG00000000154 0 0
## ENSMUSG00000000159 0 3
## ENSMUSG00000000056 0 0
## ENSMUSG00000000049 0 0
## ENSMUSG00000000168 0 0
## ENSMUSG00000000134 0 0
## ENSMUSG00000000078 0 0
## ENSMUSG00000000001 0 0
## ENSMUSG00000000194 0 0
## ENSMUSG00000000126 0 0
## ENSMUSG00000000167 0 0
## ENSMUSG00000000184 0 0
## ENSMUSG00000000088 2 0
## ENSMUSG00000000120 0 0
## ENSMUSG00000000031 0 0
## SRR5164436_AAAAAGAAAAAT SRR5164436_AAAAATGCACTA
## ENSMUSG00000000028 0 0
## ENSMUSG00000000037 0 0
## ENSMUSG00000000148 0 0
## ENSMUSG00000000142 0 0
## ENSMUSG00000000183 0 0
## ENSMUSG00000000149 0 0
## ENSMUSG00000000131 0 0
## ENSMUSG00000000125 0 0
## ENSMUSG00000000085 1 0
## ENSMUSG00000000171 0 3
## ENSMUSG00000000093 0 0
## ENSMUSG00000000157 0 0
## ENSMUSG00000000094 0 0
## ENSMUSG00000000127 0 0
## ENSMUSG00000000103 0 0
## ENSMUSG00000000182 0 0
## ENSMUSG00000000058 0 0
## ENSMUSG00000000154 0 0
## ENSMUSG00000000159 0 11
## ENSMUSG00000000056 0 0
## ENSMUSG00000000049 0 0
## ENSMUSG00000000168 0 0
## ENSMUSG00000000134 0 0
## ENSMUSG00000000078 3 0
## ENSMUSG00000000001 0 0
## ENSMUSG00000000194 0 0
## ENSMUSG00000000126 0 0
## ENSMUSG00000000167 0 0
## ENSMUSG00000000184 0 0
## ENSMUSG00000000088 0 2
## ENSMUSG00000000120 0 0
## ENSMUSG00000000031 0 0
## SRR5164436_AAAACACTTCAT
## ENSMUSG00000000028 0
## ENSMUSG00000000037 0
## ENSMUSG00000000148 0
## ENSMUSG00000000142 0
## ENSMUSG00000000183 0
## ENSMUSG00000000149 0
## ENSMUSG00000000131 2
## ENSMUSG00000000125 0
## ENSMUSG00000000085 1
## ENSMUSG00000000171 1
## ENSMUSG00000000093 0
## ENSMUSG00000000157 0
## ENSMUSG00000000094 0
## ENSMUSG00000000127 0
## ENSMUSG00000000103 0
## ENSMUSG00000000182 0
## ENSMUSG00000000058 0
## ENSMUSG00000000154 0
## ENSMUSG00000000159 14
## ENSMUSG00000000056 0
## ENSMUSG00000000049 0
## ENSMUSG00000000168 3
## ENSMUSG00000000134 0
## ENSMUSG00000000078 0
## ENSMUSG00000000001 0
## ENSMUSG00000000194 0
## ENSMUSG00000000126 0
## ENSMUSG00000000167 0
## ENSMUSG00000000184 0
## ENSMUSG00000000088 2
## ENSMUSG00000000120 0
## ENSMUSG00000000031 0dim(seurat_campbell_batch_edgeR_chow@raw.data[mito_genes_present, ])## [1] 32 11255## Calculate the percentage of mitcondrial gene counts per cell
percent_mito <- Matrix::colSums(seurat_campbell_batch_edgeR_chow@raw.data[mito_genes_present, ])/Matrix::colSums(seurat_campbell_batch_edgeR_chow@raw.data)
## Basic stats of proportion of mitocondrial gene expression per cell
summary(percent_mito)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0006089 0.0011086 0.0013382 0.0018298 0.0106698## add percentage mitocondrial genes into metadata
seurat_campbell_batch_edgeR_chow <- AddMetaData(object = seurat_campbell_batch_edgeR_chow,
metadata = percent_mito,
col.name = "percent_mito")
## Look at the seurat object meta data
head(seurat_campbell_batch_edgeR_chow@meta.data)## nGene nUMI orig.ident replicate_name percent_mito
## SRR5164436_AAAAAATGCATG 1158 2043 SRR5164436 SRR5164436 0.0014684288
## SRR5164436_AAAAACACGACG 2171 4531 SRR5164436 SRR5164436 0.0006623979
## SRR5164436_AAAAAGAAAAAT 1286 2154 SRR5164436 SRR5164436 0.0018578727
## SRR5164436_AAAAATGCACTA 3464 8755 SRR5164436 SRR5164436 0.0018289895
## SRR5164436_AAAACACTTCAT 3626 10143 SRR5164436 SRR5164436 0.0022691397
## SRR5164436_AAAACGAACATG 3775 10500 SRR5164436 SRR5164436 0.0012388031## QC plots of number of genes, UMIs, and % mitochondria
VlnPlot(object = seurat_campbell_batch_edgeR_chow,
features.plot = c("nGene", "nUMI", "percent_mito"),
nCol = 3,
x.lab.rot = TRUE,
point.size.use = 0.2
)
## QC plots to show the relationship between nUMIs and relative mitocondrial gene expression or number of genes.
par(mfrow = c(1, 2))
# GenePlot(object = seurat_campbell_batch_edgeR_chow, gene1 = "nUMI", gene2 = "percent_mito", do.hover = TRUE, pch.use = 1)
# GenePlot(object = seurat_campbell_batch_edgeR_chow, gene1 = "nUMI", gene2 = "nGene", do.hover = TRUE, pch.use = 1)
GenePlot(object = seurat_campbell_batch_edgeR_chow, gene1 = "nUMI", gene2 = "percent_mito", pch.use = 16, cex.use = 0.5)
GenePlot(object = seurat_campbell_batch_edgeR_chow, gene1 = "nUMI", gene2 = "nGene", pch.use = 16, cex.use = 0.5)
Filter cells after QC
Filter cells out with more than 0.4% of total gene expression comming from mitocondrially encoded genes and more than 6000 genes expressed.
## manual check; I already know all cells have >800 genes
table(seurat_campbell_batch_edgeR_chow@meta.data$percent_mito < 0.004 & seurat_campbell_batch_edgeR_chow@meta.data$nGene<6000)##
## FALSE TRUE
## 358 10897# From: 30000 genes across 11255 samples.
# To:
# FALSE TRUE
# 358 10897
## Filter cells with <0.4% percent_mito and <6000 genes
seurat_campbell_batch_edgeR_chow <- FilterCells(object = seurat_campbell_batch_edgeR_chow,
subset.names = c("nGene", "percent_mito"),
low.thresholds = c(800, -Inf),
high.thresholds = c(6000, 0.004))
seurat_campbell_batch_edgeR_chow## An object of class seurat in project CAMPBELL_CHOW
## 30000 genes across 10897 samples.# An object of class seurat in project CAMPBELL_CHOW
# 30000 genes across 10897 samples.
# 358 cells are filtered out; numbers consistent with aboveLog normalise gene expression per cell
## Plot graph of total expression before normalisation
hist(colSums(seurat_campbell_batch_edgeR_chow@data),
breaks = 100,
main = "Total expression before normalisation",
xlab = "Sum of expression")
## Normalise gene expression per cell
seurat_campbell_batch_edgeR_chow <- NormalizeData(object = seurat_campbell_batch_edgeR_chow, normalization.method = "LogNormalize",
scale.factor = 1e4)
## Plot graph of total expression after normalisation
hist(colSums(seurat_campbell_batch_edgeR_chow@data),
breaks = 100,
main = "Total expression after normalisation",
xlab = "Sum of expression")
## Examine data:
seurat_campbell_batch_edgeR_chow@data[1:10,1:10]## 10 x 10 sparse Matrix of class "dgCMatrix"## [[ suppressing 10 column names 'SRR5164436_AAAAAATGCATG', 'SRR5164436_AAAAACACGACG', 'SRR5164436_AAAAAGAAAAAT' ... ]]##
## ENSMUSG00000000001 . . . . . . .
## ENSMUSG00000000028 1.774064 . . . . . .
## ENSMUSG00000000031 . . . . . . .
## ENSMUSG00000000037 . . . . . . .
## ENSMUSG00000000049 . . . . . . .
## ENSMUSG00000000056 . . . . . . .
## ENSMUSG00000000058 . . . . . 1.066726 .
## ENSMUSG00000000078 . . 2.703644 . . 1.904724 .
## ENSMUSG00000000085 . . 1.730714 . 0.6864158 . .
## ENSMUSG00000000088 2.378576 . . 1.189743 1.0896270 1.066726 .
##
## ENSMUSG00000000001 . . .
## ENSMUSG00000000028 . . .
## ENSMUSG00000000031 . . .
## ENSMUSG00000000037 . . .
## ENSMUSG00000000049 . . .
## ENSMUSG00000000056 1.709448 . .
## ENSMUSG00000000058 . . .
## ENSMUSG00000000078 . . .
## ENSMUSG00000000085 . . .
## ENSMUSG00000000088 . 1.160955 .Perform batch correction using edgeR
Paper says removeBatchEffect is an edgeR function but actually it is a limma function!?
# par(mfrow=c(1,2))
# do_PCA(seurat_campbell_batch_edgeR_chow@data, plot_title="before removeBatchEffect()")
seurat_campbell_batch_edgeR_chow@data = removeBatchEffect(seurat_campbell_batch_edgeR_chow@data,
batch = seurat_campbell_batch_edgeR_chow@meta.data$replicate_name)
seurat_campbell_batch_edgeR_chow@data[1:10,1:10]## SRR5164436_AAAAAATGCATG SRR5164436_AAAAACACGACG
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 1.7742737394 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 -0.0038975079
## ENSMUSG00000000058 -0.0002263577 -0.0002263577
## ENSMUSG00000000078 -0.0071409545 -0.0071409545
## ENSMUSG00000000085 -0.0033954029 -0.0033954029
## ENSMUSG00000000088 2.3764941945 -0.0020815808
## SRR5164436_AAAAAGAAAAAT SRR5164436_AAAAATGCACTA
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 0.0002094769 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 -0.0038975079
## ENSMUSG00000000058 -0.0002263577 -0.0002263577
## ENSMUSG00000000078 2.6965025860 -0.0071409545
## ENSMUSG00000000085 1.7273184316 -0.0033954029
## ENSMUSG00000000088 -0.0020815808 1.1876615160
## SRR5164436_AAAACACTTCAT SRR5164436_AAAACGAACATG
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 0.0002094769 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 -0.0038975079
## ENSMUSG00000000058 -0.0002263577 1.0664999199
## ENSMUSG00000000078 -0.0071409545 1.8975829800
## ENSMUSG00000000085 0.6830204120 -0.0033954029
## ENSMUSG00000000088 1.0875454466 1.0646446969
## SRR5164436_AAAACGACTCAA SRR5164436_AAAACGACTCAC
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 0.0002094769 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 1.7055505837
## ENSMUSG00000000058 -0.0002263577 -0.0002263577
## ENSMUSG00000000078 -0.0071409545 -0.0071409545
## ENSMUSG00000000085 -0.0033954029 -0.0033954029
## ENSMUSG00000000088 -0.0020815808 -0.0020815808
## SRR5164436_AAAACGACTCAG SRR5164436_AAAACGACTCAT
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 0.0002094769 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 -0.0038975079
## ENSMUSG00000000058 -0.0002263577 -0.0002263577
## ENSMUSG00000000078 -0.0071409545 -0.0071409545
## ENSMUSG00000000085 -0.0033954029 -0.0033954029
## ENSMUSG00000000088 1.1588738384 -0.0020815808# do_PCA(seurat_campbell_batch_edgeR_chow@data, plot_title="after removeBatchEffect()")Find genes whose expression varies between cells.
Find genes whose expression varies between cells, which will be used to construct principal componets between cells that will be used for clustering.
## Find variable genes by expression
seurat_campbell_batch_edgeR_chow <- FindVariableGenes(object = seurat_campbell_batch_edgeR_chow,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.05,
x.high.cutoff = 4,
y.cutoff = 0.75,
num.bin = 20,
binning.method = "equal_width"
)
# number of variable genes
length(seurat_campbell_batch_edgeR_chow@var.genes)## [1] 1645seurat_campbell_batch_edgeR_chow <- FindVariableGenes(object = seurat_campbell_batch_edgeR_chow, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1645 variable genesseurat_campbell_batch_edgeR_chow <- FindVariableGenes(object = seurat_campbell_batch_edgeR_chow, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 2121 variable genesseurat_campbell_batch_edgeR_chow <- FindVariableGenes(object = seurat_campbell_batch_edgeR_chow, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 4, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 3463 variable genesseurat_campbell_batch_edgeR_chow <- FindVariableGenes(object = seurat_campbell_batch_edgeR_chow, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 4, y.cutoff = 0.5, num.bin = 300, binning.method = “equal_width” )
== 2631 variable genesScale normalised gene expression per cell to remove unwanted sources of variation
Scale gene expression per cell by building linear models for nUMI, percent_mito. Do not scale for mouse replicate as this has already been corrected for using limma?
## Scale data nUMI, percent_mito and mouse replicate
seurat_campbell_batch_edgeR_chow <- ScaleData(object = seurat_campbell_batch_edgeR_chow, vars.to.regress = c("nUMI", "percent_mito"))## Regressing out: nUMI, percent_mito##
## Time Elapsed: 49.757223367691 secs## Scaling data matrixseurat_campbell_batch_edgeR_chow@data[1:10,1:10]## SRR5164436_AAAAAATGCATG SRR5164436_AAAAACACGACG
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 1.7742737394 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 -0.0038975079
## ENSMUSG00000000058 -0.0002263577 -0.0002263577
## ENSMUSG00000000078 -0.0071409545 -0.0071409545
## ENSMUSG00000000085 -0.0033954029 -0.0033954029
## ENSMUSG00000000088 2.3764941945 -0.0020815808
## SRR5164436_AAAAAGAAAAAT SRR5164436_AAAAATGCACTA
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 0.0002094769 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 -0.0038975079
## ENSMUSG00000000058 -0.0002263577 -0.0002263577
## ENSMUSG00000000078 2.6965025860 -0.0071409545
## ENSMUSG00000000085 1.7273184316 -0.0033954029
## ENSMUSG00000000088 -0.0020815808 1.1876615160
## SRR5164436_AAAACACTTCAT SRR5164436_AAAACGAACATG
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 0.0002094769 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 -0.0038975079
## ENSMUSG00000000058 -0.0002263577 1.0664999199
## ENSMUSG00000000078 -0.0071409545 1.8975829800
## ENSMUSG00000000085 0.6830204120 -0.0033954029
## ENSMUSG00000000088 1.0875454466 1.0646446969
## SRR5164436_AAAACGACTCAA SRR5164436_AAAACGACTCAC
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 0.0002094769 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 1.7055505837
## ENSMUSG00000000058 -0.0002263577 -0.0002263577
## ENSMUSG00000000078 -0.0071409545 -0.0071409545
## ENSMUSG00000000085 -0.0033954029 -0.0033954029
## ENSMUSG00000000088 -0.0020815808 -0.0020815808
## SRR5164436_AAAACGACTCAG SRR5164436_AAAACGACTCAT
## ENSMUSG00000000001 -0.0106040862 -0.0106040862
## ENSMUSG00000000028 0.0002094769 0.0002094769
## ENSMUSG00000000031 -0.0016074305 -0.0016074305
## ENSMUSG00000000037 0.0011305821 0.0011305821
## ENSMUSG00000000049 -0.0011731937 -0.0011731937
## ENSMUSG00000000056 -0.0038975079 -0.0038975079
## ENSMUSG00000000058 -0.0002263577 -0.0002263577
## ENSMUSG00000000078 -0.0071409545 -0.0071409545
## ENSMUSG00000000085 -0.0033954029 -0.0033954029
## ENSMUSG00000000088 1.1588738384 -0.0020815808Principal component anlysis of variable genes.
Principal component anlysis of variable genes for use in cell clustering.
## Perform principal component analysis on variable genes
seurat_campbell_batch_edgeR_chow <- RunPCA(object = seurat_campbell_batch_edgeR_chow,
pc.genes = seurat_campbell_batch_edgeR_chow@var.genes,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 5)## [1] "PC1"
## [1] "ENSMUSG00000002985" "ENSMUSG00000026385" "ENSMUSG00000031765"
## [4] "ENSMUSG00000026701" "ENSMUSG00000000567"
## [1] ""
## [1] "ENSMUSG00000050711" "ENSMUSG00000027350" "ENSMUSG00000027273"
## [4] "ENSMUSG00000024261" "ENSMUSG00000000159"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000020077" "ENSMUSG00000036905" "ENSMUSG00000060802"
## [4] "ENSMUSG00000036896" "ENSMUSG00000036887"
## [1] ""
## [1] "ENSMUSG00000067786" "ENSMUSG00000055254" "ENSMUSG00000031760"
## [4] "ENSMUSG00000037852" "ENSMUSG00000026701"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000037625" "ENSMUSG00000031425" "ENSMUSG00000032854"
## [4] "ENSMUSG00000076439" "ENSMUSG00000036634"
## [1] ""
## [1] "ENSMUSG00000027447" "ENSMUSG00000036896" "ENSMUSG00000036887"
## [4] "ENSMUSG00000036905" "ENSMUSG00000030579"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000079018" "ENSMUSG00000022584" "ENSMUSG00000036256"
## [4] "ENSMUSG00000056492" "ENSMUSG00000075602"
## [1] ""
## [1] "ENSMUSG00000036896" "ENSMUSG00000036887" "ENSMUSG00000036905"
## [4] "ENSMUSG00000030579" "ENSMUSG00000058715"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000022132" "ENSMUSG00000058897" "ENSMUSG00000033737"
## [4] "ENSMUSG00000007682" "ENSMUSG00000034810"
## [1] ""
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000038370"
## [4] "ENSMUSG00000027800" "ENSMUSG00000094800"
## [1] ""
## [1] ""## visualise top genes associated with principal components
VizPCA(object = seurat_campbell_batch_edgeR_chow, pcs.use = 1:9)
## Plot principal component 1 v's 2
PCAPlot(object = seurat_campbell_batch_edgeR_chow, dim.1 = 1, dim.2 = 2)
## Plot principal component 2 v's 3
PCAPlot(object = seurat_campbell_batch_edgeR_chow, dim.1 = 2, dim.2 = 3)
## Plot heat map for gene expression of principal component 1 genes
PCHeatmap(object = seurat_campbell_batch_edgeR_chow, pc.use = 1, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 2 genes
PCHeatmap(object = seurat_campbell_batch_edgeR_chow, pc.use = 2, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 3 genes
PCHeatmap(object = seurat_campbell_batch_edgeR_chow, pc.use = 3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 4 genes
PCHeatmap(object = seurat_campbell_batch_edgeR_chow, pc.use = 4, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 5 genes
PCHeatmap(object = seurat_campbell_batch_edgeR_chow, pc.use = 5, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot multiple heatmaps of gene expression per PC genes
PCHeatmap(object = seurat_campbell_batch_edgeR_chow,
pc.use = 6:20,
cells.use = 500,
do.balanced = TRUE,
label.columns = FALSE)
## Takes a pre-computed PCA (from most variable genes identified earlier) and projects this onto the entire dataset (all genes)
seurat_campbell_batch_edgeR_chow <- ProjectPCA(object = seurat_campbell_batch_edgeR_chow, do.print = TRUE)## [1] "PC1"
## [1] "ENSMUSG00000002985" "ENSMUSG00000026385" "ENSMUSG00000031765"
## [4] "ENSMUSG00000026701" "ENSMUSG00000000567" "ENSMUSG00000050953"
## [7] "ENSMUSG00000018593" "ENSMUSG00000001025" "ENSMUSG00000031762"
## [10] "ENSMUSG00000035805" "ENSMUSG00000026728" "ENSMUSG00000067786"
## [13] "ENSMUSG00000095538" "ENSMUSG00000022528" "ENSMUSG00000058135"
## [16] "ENSMUSG00000005360" "ENSMUSG00000018102" "ENSMUSG00000027712"
## [19] "ENSMUSG00000029838" "ENSMUSG00000021250" "ENSMUSG00000030342"
## [22] "ENSMUSG00000032231" "ENSMUSG00000034467" "ENSMUSG00000008540"
## [25] "ENSMUSG00000053931" "ENSMUSG00000036570" "ENSMUSG00000026649"
## [28] "ENSMUSG00000063564" "ENSMUSG00000055254" "ENSMUSG00000017009"
## [1] ""
## [1] "ENSMUSG00000033061" "ENSMUSG00000050711" "ENSMUSG00000047261"
## [4] "ENSMUSG00000027581" "ENSMUSG00000055430" "ENSMUSG00000019986"
## [7] "ENSMUSG00000026576" "ENSMUSG00000024268" "ENSMUSG00000021087"
## [10] "ENSMUSG00000025468" "ENSMUSG00000043388" "ENSMUSG00000040785"
## [13] "ENSMUSG00000042750" "ENSMUSG00000027500" "ENSMUSG00000029223"
## [16] "ENSMUSG00000022577" "ENSMUSG00000036699" "ENSMUSG00000027350"
## [19] "ENSMUSG00000059361" "ENSMUSG00000019923" "ENSMUSG00000044349"
## [22] "ENSMUSG00000035964" "ENSMUSG00000027273" "ENSMUSG00000018965"
## [25] "ENSMUSG00000071658" "ENSMUSG00000039278" "ENSMUSG00000024261"
## [28] "ENSMUSG00000031840" "ENSMUSG00000024423" "ENSMUSG00000000159"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000020077" "ENSMUSG00000036905" "ENSMUSG00000060802"
## [4] "ENSMUSG00000036896" "ENSMUSG00000036887" "ENSMUSG00000079018"
## [7] "ENSMUSG00000075602" "ENSMUSG00000029484" "ENSMUSG00000030579"
## [10] "ENSMUSG00000028581" "ENSMUSG00000058715" "ENSMUSG00000036256"
## [13] "ENSMUSG00000026365" "ENSMUSG00000029622" "ENSMUSG00000022584"
## [16] "ENSMUSG00000024621" "ENSMUSG00000064373" "ENSMUSG00000024397"
## [19] "ENSMUSG00000056492" "ENSMUSG00000021423" "ENSMUSG00000023992"
## [22] "ENSMUSG00000015852" "ENSMUSG00000038642" "ENSMUSG00000001123"
## [25] "ENSMUSG00000040584" "ENSMUSG00000020154" "ENSMUSG00000016494"
## [28] "ENSMUSG00000041378" "ENSMUSG00000046805" "ENSMUSG00000030237"
## [1] ""
## [1] "ENSMUSG00000018451" "ENSMUSG00000021270" "ENSMUSG00000067786"
## [4] "ENSMUSG00000055254" "ENSMUSG00000026223" "ENSMUSG00000031428"
## [7] "ENSMUSG00000052727" "ENSMUSG00000037926" "ENSMUSG00000039278"
## [10] "ENSMUSG00000101111" "ENSMUSG00000031760" "ENSMUSG00000037852"
## [13] "ENSMUSG00000031633" "ENSMUSG00000026701" "ENSMUSG00000055430"
## [16] "ENSMUSG00000021379" "ENSMUSG00000019986" "ENSMUSG00000040785"
## [19] "ENSMUSG00000035805" "ENSMUSG00000046432" "ENSMUSG00000017390"
## [22] "ENSMUSG00000042750" "ENSMUSG00000034467" "ENSMUSG00000079037"
## [25] "ENSMUSG00000025666" "ENSMUSG00000015222" "ENSMUSG00000021087"
## [28] "ENSMUSG00000095538" "ENSMUSG00000026649" "ENSMUSG00000064357"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000037625" "ENSMUSG00000031425" "ENSMUSG00000032854"
## [4] "ENSMUSG00000076439" "ENSMUSG00000036634" "ENSMUSG00000026830"
## [7] "ENSMUSG00000031775" "ENSMUSG00000006782" "ENSMUSG00000046160"
## [10] "ENSMUSG00000032060" "ENSMUSG00000050121" "ENSMUSG00000033579"
## [13] "ENSMUSG00000022425" "ENSMUSG00000032517" "ENSMUSG00000027375"
## [16] "ENSMUSG00000041607" "ENSMUSG00000020486" "ENSMUSG00000027562"
## [19] "ENSMUSG00000040759" "ENSMUSG00000027858" "ENSMUSG00000013523"
## [22] "ENSMUSG00000073680" "ENSMUSG00000022090" "ENSMUSG00000026888"
## [25] "ENSMUSG00000037166" "ENSMUSG00000027199" "ENSMUSG00000043448"
## [28] "ENSMUSG00000028412" "ENSMUSG00000011884" "ENSMUSG00000090996"
## [1] ""
## [1] "ENSMUSG00000027447" "ENSMUSG00000036896" "ENSMUSG00000036887"
## [4] "ENSMUSG00000036905" "ENSMUSG00000018451" "ENSMUSG00000030579"
## [7] "ENSMUSG00000058715" "ENSMUSG00000028581" "ENSMUSG00000024621"
## [10] "ENSMUSG00000024397" "ENSMUSG00000023992" "ENSMUSG00000021423"
## [13] "ENSMUSG00000022587" "ENSMUSG00000015852" "ENSMUSG00000038642"
## [16] "ENSMUSG00000026576" "ENSMUSG00000046805" "ENSMUSG00000036353"
## [19] "ENSMUSG00000008682" "ENSMUSG00000021268" "ENSMUSG00000048163"
## [22] "ENSMUSG00000040747" "ENSMUSG00000060802" "ENSMUSG00000059498"
## [25] "ENSMUSG00000050711" "ENSMUSG00000030786" "ENSMUSG00000021665"
## [28] "ENSMUSG00000033061" "ENSMUSG00000030844" "ENSMUSG00000000682"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000079018" "ENSMUSG00000022584" "ENSMUSG00000036256"
## [4] "ENSMUSG00000056492" "ENSMUSG00000075602" "ENSMUSG00000040584"
## [7] "ENSMUSG00000020154" "ENSMUSG00000041378" "ENSMUSG00000030237"
## [10] "ENSMUSG00000030235" "ENSMUSG00000061353" "ENSMUSG00000029648"
## [13] "ENSMUSG00000039167" "ENSMUSG00000001946" "ENSMUSG00000033960"
## [16] "ENSMUSG00000031239" "ENSMUSG00000039349" "ENSMUSG00000024140"
## [19] "ENSMUSG00000020717" "ENSMUSG00000006386" "ENSMUSG00000114487"
## [22] "ENSMUSG00000042116" "ENSMUSG00000031871" "ENSMUSG00000027435"
## [25] "ENSMUSG00000019966" "ENSMUSG00000034738" "ENSMUSG00000062960"
## [28] "ENSMUSG00000045954" "ENSMUSG00000040732" "ENSMUSG00000039831"
## [1] ""
## [1] "ENSMUSG00000036896" "ENSMUSG00000036887" "ENSMUSG00000036905"
## [4] "ENSMUSG00000030579" "ENSMUSG00000058715" "ENSMUSG00000024621"
## [7] "ENSMUSG00000021423" "ENSMUSG00000028581" "ENSMUSG00000024397"
## [10] "ENSMUSG00000038642" "ENSMUSG00000023992" "ENSMUSG00000015852"
## [13] "ENSMUSG00000036353" "ENSMUSG00000046805" "ENSMUSG00000048163"
## [16] "ENSMUSG00000030786" "ENSMUSG00000059498" "ENSMUSG00000040747"
## [19] "ENSMUSG00000000682" "ENSMUSG00000021665" "ENSMUSG00000052160"
## [22] "ENSMUSG00000040229" "ENSMUSG00000036908" "ENSMUSG00000018008"
## [25] "ENSMUSG00000030844" "ENSMUSG00000069516" "ENSMUSG00000044811"
## [28] "ENSMUSG00000020377" "ENSMUSG00000089929" "ENSMUSG00000052336"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000022132" "ENSMUSG00000058897" "ENSMUSG00000033737"
## [4] "ENSMUSG00000007682" "ENSMUSG00000034810" "ENSMUSG00000055653"
## [7] "ENSMUSG00000029838" "ENSMUSG00000022528" "ENSMUSG00000045005"
## [10] "ENSMUSG00000063564" "ENSMUSG00000030629" "ENSMUSG00000022816"
## [13] "ENSMUSG00000026185" "ENSMUSG00000017493" "ENSMUSG00000052387"
## [16] "ENSMUSG00000047786" "ENSMUSG00000024518" "ENSMUSG00000021250"
## [19] "ENSMUSG00000017390" "ENSMUSG00000064351" "ENSMUSG00000022419"
## [22] "ENSMUSG00000037206" "ENSMUSG00000030111" "ENSMUSG00000064370"
## [25] "ENSMUSG00000034640" "ENSMUSG00000008540" "ENSMUSG00000027239"
## [28] "ENSMUSG00000061718" "ENSMUSG00000093460" "ENSMUSG00000038418"
## [1] ""
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000038370"
## [4] "ENSMUSG00000027800" "ENSMUSG00000094800" "ENSMUSG00000044772"
## [7] "ENSMUSG00000027744" "ENSMUSG00000032595" "ENSMUSG00000072674"
## [10] "ENSMUSG00000110332" "ENSMUSG00000045655" "ENSMUSG00000041323"
## [13] "ENSMUSG00000047139" "ENSMUSG00000108841" "ENSMUSG00000095304"
## [16] "ENSMUSG00000022037" "ENSMUSG00000047394" "ENSMUSG00000029182"
## [19] "ENSMUSG00000046242" "ENSMUSG00000072473" "ENSMUSG00000047671"
## [22] "ENSMUSG00000044475" "ENSMUSG00000027360" "ENSMUSG00000020473"
## [25] "ENSMUSG00000028441" "ENSMUSG00000026683" "ENSMUSG00000038135"
## [28] "ENSMUSG00000026301" "ENSMUSG00000091345" "ENSMUSG00000027867"
## [1] ""
## [1] ""Determine statistically significant principal components
## Perform jackstraw statistical test to investigate statistically significant PC.
seurat_campbell_batch_edgeR_chow <- JackStraw(object = seurat_campbell_batch_edgeR_chow,
num.replicate = 100,
display.progress = TRUE
)##
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## Time Elapsed: 6.37038615544637 mins# Maximum number of PCs allowed = 20.
## Visualise JackStraw plots
JackStrawPlot(object = seurat_campbell_batch_edgeR_chow, PCs = 1:20)## Warning: Removed 23020 rows containing missing values (geom_point).
## An object of class seurat in project CAMPBELL_CHOW
## 30000 genes across 10897 samples.## A less computationally intensive heuristic method for finding the statistically significant PCAs is using an elbow plot
PCElbowPlot(object = seurat_campbell_batch_edgeR_chow)
Using the limma batch corrected data 20 PC’s are still significant.
Cell clustering
## Cluster cells by PC
seurat_campbell_batch_edgeR_chow <- FindClusters(object = seurat_campbell_batch_edgeR_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_batch_edgeR_chow)## Parameters used in latest FindClusters calculation run on: 2018-11-20 20:32:18
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_batch_edgeR_chow <- RunTSNE(object = seurat_campbell_batch_edgeR_chow, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_batch_edgeR_chow)
TSNEPlot(object = seurat_campbell_batch_edgeR_chow,
no.legend = TRUE,
do.label = TRUE)
## 1645 variable genes = 19 clusters
table(seurat_campbell_batch_edgeR_chow@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 3612 1755 1398 613 553 513 343 323 303 263 250 210 188 165 135
## 15 16 17 18
## 107 64 58 44proportion_table<- table(seurat_campbell_batch_edgeR_chow@ident, seurat_campbell_batch_edgeR_chow@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164436 SRR5164437 SRR5164438 SRR5164443 SRR5164445
## 0 0.28433 0.41417 0.27423 0.43832 0.36663
## 1 0.16553 0.12386 0.23042 0.09974 0.08438
## 2 0.11795 0.14759 0.12349 0.17585 0.10184
## 3 0.06838 0.03251 0.06830 0.03150 0.06111
## 4 0.04615 0.05202 0.05795 0.03412 0.04850
## 5 0.03989 0.05657 0.02415 0.03675 0.11154
## 6 0.04046 0.02341 0.03829 0.00787 0.01455
## 7 0.02764 0.02991 0.02932 0.03675 0.03395
## 8 0.04330 0.01528 0.03277 0.01050 0.00485
## 9 0.02507 0.02568 0.01656 0.04199 0.03104
## 10 0.02707 0.02438 0.02346 0.00787 0.00873
## 11 0.03048 0.00748 0.02518 0.00262 0.00582
## 12 0.02137 0.01073 0.01621 0.01575 0.02619
## 13 0.01880 0.01040 0.01069 0.02100 0.02716
## 14 0.01425 0.01300 0.01345 0.01575 0.00000
## 15 0.00712 0.00260 0.00000 0.02100 0.06402
## 16 0.00969 0.00423 0.00379 0.00262 0.00485
## 17 0.00627 0.00488 0.00586 0.00000 0.00388
## 18 0.00627 0.00130 0.00586 0.00000 0.00097TSNEPlot(object = seurat_campbell_batch_edgeR_chow,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = 5))
colnames(proportion_table)<- colnames(table(seurat_campbell_batch_edgeR_chow@ident,
seurat_campbell_batch_edgeR_chow@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_batch_edgeR_chow <- FindClusters(object = seurat_campbell_batch_edgeR_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_batch_edgeR_chow)## Parameters used in latest FindClusters calculation run on: 2018-11-20 20:32:18
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_batch_edgeR_chow <- RunTSNE(object = seurat_campbell_batch_edgeR_chow, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_batch_edgeR_chow)
TSNEPlot(object = seurat_campbell_batch_edgeR_chow,
no.legend = TRUE,
do.label = TRUE)
## 1645 variable genes = 20 clusters
table(seurat_campbell_batch_edgeR_chow@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 2075 1730 1483 1397 615 593 529 343 322 303 286 250 210 188 165
## 15 16 17 18 19
## 135 107 64 58 44proportion_table<- table(seurat_campbell_batch_edgeR_chow@ident, seurat_campbell_batch_edgeR_chow@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164436 SRR5164437 SRR5164438 SRR5164443 SRR5164445
## 0 0.11140 0.34590 0.08037 0.38845 0.23181
## 1 0.16439 0.12126 0.22732 0.09974 0.08050
## 2 0.16752 0.06307 0.18800 0.05249 0.13191
## 3 0.11795 0.14759 0.12315 0.17585 0.10184
## 4 0.06838 0.03251 0.06864 0.03150 0.06208
## 5 0.04957 0.05754 0.06106 0.03412 0.05044
## 6 0.04188 0.05624 0.02760 0.03412 0.11251
## 7 0.04046 0.02341 0.03829 0.00787 0.01455
## 8 0.02764 0.02991 0.02898 0.03675 0.03395
## 9 0.04330 0.01528 0.03277 0.01050 0.00485
## 10 0.02621 0.02828 0.01932 0.04199 0.03395
## 11 0.02707 0.02438 0.02346 0.00787 0.00873
## 12 0.03048 0.00748 0.02518 0.00262 0.00582
## 13 0.02137 0.01073 0.01621 0.01575 0.02619
## 14 0.01880 0.01040 0.01069 0.02100 0.02716
## 15 0.01425 0.01300 0.01345 0.01575 0.00000
## 16 0.00712 0.00260 0.00000 0.02100 0.06402
## 17 0.00969 0.00423 0.00379 0.00262 0.00485
## 18 0.00627 0.00488 0.00586 0.00000 0.00388
## 19 0.00627 0.00130 0.00586 0.00000 0.00097TSNEPlot(object = seurat_campbell_batch_edgeR_chow,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = 5))
colnames(proportion_table)<- colnames(table(seurat_campbell_batch_edgeR_chow@ident,
seurat_campbell_batch_edgeR_chow@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_batch_edgeR_chow <- FindClusters(object = seurat_campbell_batch_edgeR_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 1.5, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_batch_edgeR_chow)## Parameters used in latest FindClusters calculation run on: 2018-11-20 20:32:18
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_batch_edgeR_chow <- RunTSNE(object = seurat_campbell_batch_edgeR_chow, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_batch_edgeR_chow)
TSNEPlot(object = seurat_campbell_batch_edgeR_chow,
no.legend = TRUE,
do.label = TRUE)
## 1645 variable genes = 24 clusters
table(seurat_campbell_batch_edgeR_chow@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1741 1071 934 763 742 622 609 607 600 547 343 328 303 256 250
## 15 16 17 18 19 20 21 22 23
## 210 209 188 165 136 107 64 58 44proportion_table<- table(seurat_campbell_batch_edgeR_chow@ident, seurat_campbell_batch_edgeR_chow@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164436 SRR5164437 SRR5164438 SRR5164443 SRR5164445
## 0 0.09345 0.29746 0.06278 0.30446 0.19399
## 1 0.10940 0.06534 0.14384 0.05249 0.04753
## 2 0.12479 0.00878 0.14453 0.01575 0.04268
## 3 0.03989 0.11834 0.03518 0.16273 0.09214
## 4 0.05983 0.06404 0.09314 0.06037 0.04074
## 5 0.07778 0.02731 0.08658 0.01312 0.00873
## 6 0.06781 0.03218 0.06795 0.03150 0.06111
## 7 0.04644 0.06144 0.04795 0.04462 0.09602
## 8 0.05043 0.05787 0.06209 0.03412 0.05044
## 9 0.04217 0.05819 0.02863 0.03937 0.11833
## 10 0.04046 0.02341 0.03829 0.00787 0.01455
## 11 0.02764 0.03088 0.02967 0.03675 0.03492
## 12 0.04330 0.01528 0.03277 0.01050 0.00485
## 13 0.01339 0.03999 0.01138 0.07087 0.02522
## 14 0.02707 0.02438 0.02346 0.00787 0.00873
## 15 0.03048 0.00748 0.02518 0.00262 0.00582
## 16 0.02194 0.02048 0.01035 0.02887 0.02716
## 17 0.02137 0.01073 0.01621 0.01575 0.02619
## 18 0.01880 0.01040 0.01069 0.02100 0.02716
## 19 0.01425 0.01300 0.01380 0.01575 0.00000
## 20 0.00712 0.00260 0.00000 0.02100 0.06402
## 21 0.00969 0.00423 0.00379 0.00262 0.00485
## 22 0.00627 0.00488 0.00586 0.00000 0.00388
## 23 0.00627 0.00130 0.00586 0.00000 0.00097TSNEPlot(object = seurat_campbell_batch_edgeR_chow,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = 5))
colnames(proportion_table)<- colnames(table(seurat_campbell_batch_edgeR_chow@ident,
seurat_campbell_batch_edgeR_chow@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_batch_edgeR_chow <- FindClusters(object = seurat_campbell_batch_edgeR_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 2.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_batch_edgeR_chow)## Parameters used in latest FindClusters calculation run on: 2018-11-20 20:32:18
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_batch_edgeR_chow <- RunTSNE(object = seurat_campbell_batch_edgeR_chow, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_batch_edgeR_chow)
TSNEPlot(object = seurat_campbell_batch_edgeR_chow,
no.legend = TRUE,
do.label = TRUE)
## 1645 variable genes = 29 clusters
table(seurat_campbell_batch_edgeR_chow@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 924 902 880 870 758 648 622 613 605 501 343 325 310 303 290 286 273 250
## 18 19 20 21 22 23 24 25 26 27 28
## 222 188 170 136 110 107 64 58 55 44 40proportion_table<- table(seurat_campbell_batch_edgeR_chow@ident, seurat_campbell_batch_edgeR_chow@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164436 SRR5164437 SRR5164438 SRR5164443 SRR5164445
## 0 0.12251 0.00975 0.14315 0.01837 0.04074
## 1 0.05071 0.15085 0.03587 0.12861 0.10378
## 2 0.04672 0.14824 0.03036 0.18373 0.09893
## 3 0.08405 0.05559 0.12660 0.03412 0.02328
## 4 0.03960 0.11736 0.03518 0.16273 0.09117
## 5 0.06382 0.04909 0.07347 0.03937 0.04365
## 6 0.07778 0.02731 0.08658 0.01312 0.00873
## 7 0.04701 0.06339 0.04795 0.03937 0.09602
## 8 0.06695 0.03218 0.06761 0.03150 0.06111
## 9 0.03989 0.05267 0.02518 0.02887 0.11154
## 10 0.04046 0.02341 0.03829 0.00787 0.01455
## 11 0.02764 0.03023 0.02932 0.03675 0.03492
## 12 0.02593 0.02893 0.03484 0.02362 0.01940
## 13 0.04330 0.01528 0.03277 0.01050 0.00485
## 14 0.02422 0.02893 0.02691 0.01050 0.03298
## 15 0.02621 0.02828 0.01932 0.04199 0.03395
## 16 0.01396 0.04259 0.01345 0.07612 0.02425
## 17 0.02707 0.02438 0.02346 0.00787 0.00873
## 18 0.01795 0.01691 0.02829 0.02625 0.01455
## 19 0.02137 0.01073 0.01621 0.01575 0.02619
## 20 0.02593 0.00423 0.02035 0.00262 0.00582
## 21 0.01425 0.01300 0.01380 0.01575 0.00000
## 22 0.01026 0.00975 0.00586 0.01312 0.02134
## 23 0.00712 0.00260 0.00000 0.02100 0.06402
## 24 0.00969 0.00423 0.00379 0.00262 0.00485
## 25 0.00627 0.00488 0.00586 0.00000 0.00388
## 26 0.00855 0.00065 0.00483 0.00787 0.00582
## 27 0.00627 0.00130 0.00586 0.00000 0.00097
## 28 0.00456 0.00325 0.00483 0.00000 0.00000TSNEPlot(object = seurat_campbell_batch_edgeR_chow,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4 )
# install.packages("PerformanceAnalytics")
proportion_table<- data.frame(matrix(proportion_table, ncol = 5))
colnames(proportion_table)<- colnames(table(seurat_campbell_batch_edgeR_chow@ident,
seurat_campbell_batch_edgeR_chow@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
# cor(proportion_table[1],proportion_table[4])
## Cluster cells using final parameters (1645 genes, 20 PC, resolution = 0.6)
seurat_campbell_batch_edgeR_chow <- FindClusters(object = seurat_campbell_batch_edgeR_chow, reduction.type = "pca", dims.use = 1:20,
resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
## Produce t-SNE
seurat_campbell_batch_edgeR_chow <- RunTSNE(object = seurat_campbell_batch_edgeR_chow, dims.use = 1:20, do.fast = TRUE)Use 1645 genes, 20 principal components and a resolution of 0.6 to give 19 individual clusters. Although, there is good correlation between the proportion of cells in each cluster from each experimental batch using a resolution of 0.6, there is still batch effects visible. Therefore, the results of using the limma batch correction feature is similar to using the seurat v2.0 scaledata batch correction (maybe a both use a linear model) so there is no benefit to using the limma feature like the Campbell paper.
Finding differentially expressed genes between cell clusters (cluster biomarkers)
## Find markers for every cluster compared to all remaining cells, report both positive and negative genes.
seurat_campbell_batch_edgeR_chow_biomarkers <- FindAllMarkers(object = seurat_campbell_batch_edgeR_chow, only.pos = FALSE, min.pct = 0.2)
## Get the top 10 biomarkers per cluster
top10_seurat_campbell_markers<- seurat_campbell_batch_edgeR_chow_biomarkers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers## # A tibble: 190 x 7
## # Groups: cluster [19]
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0. 1.01 0.948 0.842 0. 0 ENSMUSG00000027500
## 2 0. 0.993 0.976 0.875 0. 0 ENSMUSG00000047261
## 3 0. 0.915 0.995 0.903 0. 0 ENSMUSG00000055430
## 4 6.76e-261 0.865 0.779 0.562 2.03e-256 0 ENSMUSG00000048978
## 5 3.10e-256 0.913 0.946 0.921 9.31e-252 0 ENSMUSG00000066392
## 6 4.94e- 75 0.960 0.686 0.683 1.48e- 70 0 ENSMUSG00000036357
## 7 1.47e- 50 1.56 0.744 0.763 4.40e- 46 0 ENSMUSG00000021647
## 8 4.69e- 13 2.48 0.697 0.756 1.41e- 8 0 ENSMUSG00000020660
## 9 1.27e- 12 1.56 0.54 0.474 3.81e- 8 0 ENSMUSG00000032291
## 10 1.26e- 4 1.41 0.858 0.895 1.00e+ 0 0 ENSMUSG00000037727
## # ... with 180 more rowswrite.csv(as.data.frame(seurat_campbell_batch_edgeR_chow_biomarkers), file = "seurat_campbell_batch_edgeR_chow_biomarkers.csv", quote = FALSE)
write.csv(as.data.frame(top10_seurat_campbell_markers), file = "top10_seurat_campbell_batch_edgeR_chow_biomarkers.csv", quote = FALSE)
## Perform ROC DE test. This can take a long time.
seurat_campbell_batch_edgeR_chow_biomarkers_ROC <- FindAllMarkers(object = seurat_campbell_batch_edgeR_chow, only.pos = FALSE, min.pct = 0.2, test.use = "roc")
top10_seurat_campbell_markers_ROC<- seurat_campbell_batch_edgeR_chow_biomarkers_ROC %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers_ROC## # A tibble: 190 x 9
## # Groups: cluster [19]
## myAUC avg_diff power avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0.82 0.915 0.640 0.915 0.995 0.903 NA 0 ENSMUSG00…
## 2 0.808 0.993 0.616 0.993 0.976 0.875 NA 0 ENSMUSG00…
## 3 0.8 0.799 0.6 0.799 0.993 0.89 NA 0 ENSMUSG00…
## 4 0.787 1.01 0.574 1.01 0.948 0.842 NA 0 ENSMUSG00…
## 5 0.76 0.762 0.52 0.762 0.979 0.892 NA 0 ENSMUSG00…
## 6 0.733 0.749 0.466 0.749 0.939 0.855 NA 0 ENSMUSG00…
## 7 0.73 0.776 0.46 0.776 0.916 0.84 NA 0 ENSMUSG00…
## 8 0.721 0.796 0.442 0.796 0.991 0.953 NA 0 ENSMUSG00…
## 9 0.714 0.779 0.428 0.779 0.962 0.938 NA 0 ENSMUSG00…
## 10 0.701 0.865 0.402 0.865 0.779 0.562 NA 0 ENSMUSG00…
## # ... with 180 more rowswrite.csv(as.data.frame(seurat_campbell_batch_edgeR_chow_biomarkers_ROC), file = "seurat_campbell_batch_edgeR_chow_biomarkers_ROC.csv", quote = FALSE)
write.csv(as.data.frame(top10_seurat_campbell_markers_ROC), file = "top10_seurat_campbell_batch_edgeR_chow_biomarkers_ROC.csv", quote = FALSE)
## Plot heatmap of top 10 DE genes
DoHeatmap(object = seurat_campbell_batch_edgeR_chow,
genes.use = top10_seurat_campbell_markers$gene,
slim.col.label = TRUE,
remove.key = TRUE)
Save seurat object of chow mice.
## save seurat object as .rds
saveRDS(seurat_campbell_batch_edgeR_chow, file = "./seurat_campbell_batch_edgeR_chow_final.rds")Analysis of Campbell scRNAseq data for fasted mice (3 replicates) using seurat
Load FASTED mouse data
## Load seurat object
seurat_campbell_fasted<- readRDS(file = "./seurat_campbell_fasted_just_created.rds")
seurat_campbell_fasted## An object of class seurat in project CAMPBELL_FASTED
## 21789 genes across 3783 samples.Quality control on CHOW mouse data
## Import list of mouse mitocondrially encoded genes:
mito_genes<- read.csv("mito_genes_table.csv", header = TRUE)
## Make list of mitocondrial gene IDS:
mito_genes <- mito_genes$id_with_url
## Identify which mitochondrially expressed gene IDs are present in my dataset:
mito_genes_present<-seurat_campbell_fasted@raw.data[mito_genes, ]
mito_genes_present<- mito_genes[!grepl("NA", rownames(mito_genes_present))]
seurat_campbell_fasted@raw.data[mito_genes_present, 1:5]## SRR5164441_AAAAAGGGATGC SRR5164441_AAAAATTCGGGC
## ENSMUSG00000000028 0 0
## ENSMUSG00000000037 0 0
## ENSMUSG00000000159 1 3
## ENSMUSG00000000149 0 0
## ENSMUSG00000000223 0 0
## ENSMUSG00000000167 0 0
## ENSMUSG00000000142 0 0
## ENSMUSG00000000127 0 0
## ENSMUSG00000000085 0 0
## ENSMUSG00000000202 0 0
## ENSMUSG00000000093 0 0
## ENSMUSG00000000171 0 0
## ENSMUSG00000000120 0 0
## ENSMUSG00000000134 0 0
## ENSMUSG00000000125 0 0
## ENSMUSG00000000214 0 0
## ENSMUSG00000000058 0 0
## ENSMUSG00000000168 0 0
## ENSMUSG00000000184 0 0
## ENSMUSG00000000056 0 0
## ENSMUSG00000000049 0 0
## ENSMUSG00000000197 0 1
## ENSMUSG00000000148 0 0
## ENSMUSG00000000078 0 0
## ENSMUSG00000000001 0 0
## ENSMUSG00000000247 0 0
## ENSMUSG00000000131 0 0
## ENSMUSG00000000194 0 0
## ENSMUSG00000000244 0 0
## ENSMUSG00000000088 0 1
## ENSMUSG00000000126 0 0
## ENSMUSG00000000031 0 0
## SRR5164441_AAAACGGAAACT SRR5164441_AAAACTACAACT
## ENSMUSG00000000028 0 0
## ENSMUSG00000000037 0 0
## ENSMUSG00000000159 14 0
## ENSMUSG00000000149 0 0
## ENSMUSG00000000223 0 0
## ENSMUSG00000000167 0 0
## ENSMUSG00000000142 0 2
## ENSMUSG00000000127 0 0
## ENSMUSG00000000085 0 0
## ENSMUSG00000000202 0 0
## ENSMUSG00000000093 0 0
## ENSMUSG00000000171 0 0
## ENSMUSG00000000120 0 0
## ENSMUSG00000000134 0 0
## ENSMUSG00000000125 0 0
## ENSMUSG00000000214 0 0
## ENSMUSG00000000058 0 0
## ENSMUSG00000000168 0 0
## ENSMUSG00000000184 0 0
## ENSMUSG00000000056 0 0
## ENSMUSG00000000049 0 0
## ENSMUSG00000000197 1 0
## ENSMUSG00000000148 0 0
## ENSMUSG00000000078 0 0
## ENSMUSG00000000001 0 0
## ENSMUSG00000000247 0 0
## ENSMUSG00000000131 0 0
## ENSMUSG00000000194 0 0
## ENSMUSG00000000244 0 0
## ENSMUSG00000000088 0 0
## ENSMUSG00000000126 0 0
## ENSMUSG00000000031 0 0
## SRR5164441_AAAACTGGTTAT
## ENSMUSG00000000028 0
## ENSMUSG00000000037 0
## ENSMUSG00000000159 0
## ENSMUSG00000000149 0
## ENSMUSG00000000223 0
## ENSMUSG00000000167 0
## ENSMUSG00000000142 0
## ENSMUSG00000000127 0
## ENSMUSG00000000085 0
## ENSMUSG00000000202 0
## ENSMUSG00000000093 0
## ENSMUSG00000000171 0
## ENSMUSG00000000120 0
## ENSMUSG00000000134 0
## ENSMUSG00000000125 0
## ENSMUSG00000000214 0
## ENSMUSG00000000058 0
## ENSMUSG00000000168 0
## ENSMUSG00000000184 0
## ENSMUSG00000000056 0
## ENSMUSG00000000049 0
## ENSMUSG00000000197 0
## ENSMUSG00000000148 0
## ENSMUSG00000000078 0
## ENSMUSG00000000001 0
## ENSMUSG00000000247 0
## ENSMUSG00000000131 0
## ENSMUSG00000000194 0
## ENSMUSG00000000244 0
## ENSMUSG00000000088 0
## ENSMUSG00000000126 0
## ENSMUSG00000000031 0dim(seurat_campbell_fasted@raw.data[mito_genes_present, ])## [1] 32 3783## Calculate the percentage of mitcondrial gene counts per cell
percent_mito <- Matrix::colSums(seurat_campbell_fasted@raw.data[mito_genes_present, ])/Matrix::colSums(seurat_campbell_fasted@raw.data)
## Basic stats of proportion of mitocondrial gene expression per cell
summary(percent_mito)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0007047 0.0013918 0.0016627 0.0023381 0.0120724## add percentage mitocondrial genes into metadata
seurat_campbell_fasted <- AddMetaData(object = seurat_campbell_fasted,
metadata = percent_mito,
col.name = "percent_mito")
## Look at the seurat object meta data
head(seurat_campbell_fasted@meta.data)## nGene nUMI orig.ident replicate_name percent_mito
## SRR5164441_AAAAAGGGATGC 1001 1632 SRR5164441 SRR5164441 0.0006127451
## SRR5164441_AAAAATTCGGGC 1789 3300 SRR5164441 SRR5164441 0.0015165302
## SRR5164441_AAAACGGAAACT 1833 3370 SRR5164441 SRR5164441 0.0044589774
## SRR5164441_AAAACTACAACT 1653 2753 SRR5164441 SRR5164441 0.0007278020
## SRR5164441_AAAACTGGTTAT 834 1639 SRR5164441 SRR5164441 0.0000000000
## SRR5164441_AAAACTTCTACA 1334 2115 SRR5164441 SRR5164441 0.0033112583## QC plots of number of genes, UMIs, and % mitochondria
VlnPlot(object = seurat_campbell_fasted,
features.plot = c("nGene", "nUMI", "percent_mito"),
nCol = 3,
x.lab.rot = TRUE,
point.size.use = 0.2
)
## QC plots to show the relationship between nUMIs and relative mitocondrial gene expression or number of genes.
par(mfrow = c(1, 2))
# GenePlot(object = seurat_campbell_fasted, gene1 = "nUMI", gene2 = "percent_mito", do.hover = TRUE, pch.use = 1)
# GenePlot(object = seurat_campbell_fasted, gene1 = "nUMI", gene2 = "nGene", do.hover = TRUE, pch.use = 1)
GenePlot(object = seurat_campbell_fasted, gene1 = "nUMI", gene2 = "percent_mito", pch.use = 16, cex.use = 0.5)
GenePlot(object = seurat_campbell_fasted, gene1 = "nUMI", gene2 = "nGene", pch.use = 16, cex.use = 0.5)
Filter cells after QC
Filter cells out with more than 0.5% of total gene expression comming from mitocondrially encoded genes and more than 4000 genes expressed.
## manual check; I already know all cells have >800 genes
table(seurat_campbell_fasted@meta.data$percent_mito < 0.005 & seurat_campbell_fasted@meta.data$nGene<4000)##
## FALSE TRUE
## 143 3640# FALSE TRUE
# 143 3640
## Filter cells with <0.5% percent_mito and <4000 genes
seurat_campbell_fasted <- FilterCells(object = seurat_campbell_fasted,
subset.names = c("nGene", "percent_mito"),
low.thresholds = c(800, -Inf),
high.thresholds = c(4000, 0.005))
seurat_campbell_fasted## An object of class seurat in project CAMPBELL_FASTED
## 21789 genes across 3640 samples.# An object of class seurat in project CAMPBELL_FASTED
# 21789 genes across 3640 samples.Log normalise gene expression per cell
## Plot graph of total expression before normalisation
hist(colSums(seurat_campbell_fasted@data),
breaks = 100,
main = "Total expression before normalisation",
xlab = "Sum of expression")
## Normalise gene expression per cell
seurat_campbell_fasted <- NormalizeData(object = seurat_campbell_fasted, normalization.method = "LogNormalize", scale.factor = 10000)
## Plot graph of total expression after normalisation
hist(colSums(as.data.frame(as.matrix(seurat_campbell_fasted@data))),
breaks = 100,
main = "Total expression after normalisation",
xlab = "Sum of expression")
Find genes whose expression varies between cells.
Find genes whose expression varies between cells, which will be used to construct principal componets between cells that will be used for clustering.
## Find variable genes by expression
seurat_campbell_fasted <- FindVariableGenes(object = seurat_campbell_fasted,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.05,
x.high.cutoff = 3,
y.cutoff = 0.75,
num.bin = 20,
binning.method = "equal_width"
)
# number of variable genes
length(seurat_campbell_fasted@var.genes)## [1] 1379seurat_campbell_fasted <- FindVariableGenes(object = seurat_campbell_fasted, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1384 variable genesseurat_campbell_fasted <- FindVariableGenes(object = seurat_campbell_fasted, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 4, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 2356 variable genesseurat_campbell_fasted <- FindVariableGenes(object = seurat_campbell_fasted, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1848 variable genesseurat_campbell_fasted <- FindVariableGenes(object = seurat_campbell_fasted, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 3006 variable genesseurat_campbell_fasted <- FindVariableGenes(object = seurat_campbell_fasted, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 40, binning.method = “equal_width” )
== 2870 variable geneseurat_campbell_fasted <- FindVariableGenes(object = seurat_campbell_fasted, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 3006 variable genesScale normalised gene expression per cell to remove unwanted sources of variation
Scale gene expression per cell by building linear models for nUMI, percent_mito and mouse replicate.
## Scale data nUMI, percent_mito and mouse replicate
seurat_campbell_fasted <- ScaleData(object = seurat_campbell_fasted, vars.to.regress = c("nUMI", "percent_mito", "replicate_name"))## Regressing out: nUMI, percent_mito, replicate_name##
## Time Elapsed: 29.1311025619507 secs## Scaling data matrixPrincipal component anlysis of variable genes.
Principal component anlysis of variable genes for use in cell clustering.
## Perform principal component analysis on variable genes
seurat_campbell_fasted <- RunPCA(object = seurat_campbell_fasted,
pc.genes = seurat_campbell_fasted@var.genes,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 5)## [1] "PC1"
## [1] "ENSMUSG00000026385" "ENSMUSG00000018593" "ENSMUSG00000031765"
## [4] "ENSMUSG00000050953" "ENSMUSG00000000567"
## [1] ""
## [1] "ENSMUSG00000027350" "ENSMUSG00000021700" "ENSMUSG00000026787"
## [4] "ENSMUSG00000066392" "ENSMUSG00000027523"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000041323"
## [4] "ENSMUSG00000110332" "ENSMUSG00000020473"
## [1] ""
## [1] "ENSMUSG00000027375" "ENSMUSG00000079018" "ENSMUSG00000075602"
## [4] "ENSMUSG00000022584" "ENSMUSG00000024140"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000037625" "ENSMUSG00000026830" "ENSMUSG00000036634"
## [4] "ENSMUSG00000076439" "ENSMUSG00000032517"
## [1] ""
## [1] "ENSMUSG00000079018" "ENSMUSG00000075602" "ENSMUSG00000040584"
## [4] "ENSMUSG00000022584" "ENSMUSG00000041378"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000022528" "ENSMUSG00000063564" "ENSMUSG00000007682"
## [4] "ENSMUSG00000028195" "ENSMUSG00000045005"
## [1] ""
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000094800"
## [4] "ENSMUSG00000038370" "ENSMUSG00000027800"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000029838" "ENSMUSG00000030428" "ENSMUSG00000055254"
## [4] "ENSMUSG00000045092" "ENSMUSG00000056492"
## [1] ""
## [1] "ENSMUSG00000038642" "ENSMUSG00000036905" "ENSMUSG00000036896"
## [4] "ENSMUSG00000024621" "ENSMUSG00000036887"
## [1] ""
## [1] ""## visualise top genes associated with principal components
VizPCA(object = seurat_campbell_fasted, pcs.use = 1:9)
## Plot principal component 1 v's 2
PCAPlot(object = seurat_campbell_fasted, dim.1 = 1, dim.2 = 2)
## Plot principal component 2 v's 3
PCAPlot(object = seurat_campbell_fasted, dim.1 = 2, dim.2 = 3)
## Plot heat map for gene expression of principal component 1 genes
PCHeatmap(object = seurat_campbell_fasted, pc.use = 1, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 2 genes
PCHeatmap(object = seurat_campbell_fasted, pc.use = 2, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 3 genes
PCHeatmap(object = seurat_campbell_fasted, pc.use = 3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 4 genes
PCHeatmap(object = seurat_campbell_fasted, pc.use = 4, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot multiple heatmaps of gene expression per PC genes
PCHeatmap(object = seurat_campbell_fasted,
pc.use = 5:18,
cells.use = 500,
do.balanced = TRUE,
label.columns = FALSE)
## Takes a pre-computed PCA (from most variable genes identified earlier) and projects this onto the entire dataset (all genes)
seurat_campbell_fasted <- ProjectPCA(object = seurat_campbell_fasted, do.print = TRUE)## [1] "PC1"
## [1] "ENSMUSG00000026385" "ENSMUSG00000002985" "ENSMUSG00000018593"
## [4] "ENSMUSG00000031765" "ENSMUSG00000050953" "ENSMUSG00000000567"
## [7] "ENSMUSG00000031762" "ENSMUSG00000026728" "ENSMUSG00000058135"
## [10] "ENSMUSG00000034467" "ENSMUSG00000070306" "ENSMUSG00000043164"
## [13] "ENSMUSG00000001025" "ENSMUSG00000035805" "ENSMUSG00000005360"
## [16] "ENSMUSG00000030342" "ENSMUSG00000094800" "ENSMUSG00000095538"
## [19] "ENSMUSG00000041323" "ENSMUSG00000017009" "ENSMUSG00000033208"
## [22] "ENSMUSG00000027800" "ENSMUSG00000019232" "ENSMUSG00000026649"
## [25] "ENSMUSG00000110332" "ENSMUSG00000026701" "ENSMUSG00000053931"
## [28] "ENSMUSG00000020473" "ENSMUSG00000024411" "ENSMUSG00000008540"
## [1] ""
## [1] "ENSMUSG00000021268" "ENSMUSG00000044349" "ENSMUSG00000019986"
## [4] "ENSMUSG00000050711" "ENSMUSG00000033061" "ENSMUSG00000040785"
## [7] "ENSMUSG00000055430" "ENSMUSG00000021087" "ENSMUSG00000026576"
## [10] "ENSMUSG00000043388" "ENSMUSG00000024268" "ENSMUSG00000064341"
## [13] "ENSMUSG00000027581" "ENSMUSG00000019923" "ENSMUSG00000047261"
## [16] "ENSMUSG00000027273" "ENSMUSG00000042750" "ENSMUSG00000002265"
## [19] "ENSMUSG00000107169" "ENSMUSG00000029223" "ENSMUSG00000027350"
## [22] "ENSMUSG00000033981" "ENSMUSG00000043384" "ENSMUSG00000025468"
## [25] "ENSMUSG00000024261" "ENSMUSG00000027500" "ENSMUSG00000024423"
## [28] "ENSMUSG00000060188" "ENSMUSG00000000159" "ENSMUSG00000025579"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000070306" "ENSMUSG00000021268" "ENSMUSG00000026223"
## [4] "ENSMUSG00000043164" "ENSMUSG00000019986" "ENSMUSG00000040785"
## [7] "ENSMUSG00000021270" "ENSMUSG00000055430" "ENSMUSG00000021087"
## [10] "ENSMUSG00000041323" "ENSMUSG00000110332" "ENSMUSG00000020473"
## [13] "ENSMUSG00000044772" "ENSMUSG00000044349" "ENSMUSG00000031428"
## [16] "ENSMUSG00000022037" "ENSMUSG00000027744" "ENSMUSG00000043384"
## [19] "ENSMUSG00000024033" "ENSMUSG00000036438" "ENSMUSG00000032595"
## [22] "ENSMUSG00000050711" "ENSMUSG00000033061" "ENSMUSG00000042750"
## [25] "ENSMUSG00000094800" "ENSMUSG00000034467" "ENSMUSG00000009281"
## [28] "ENSMUSG00000072674" "ENSMUSG00000026576" "ENSMUSG00000047394"
## [1] ""
## [1] "ENSMUSG00000027375" "ENSMUSG00000079018" "ENSMUSG00000075602"
## [4] "ENSMUSG00000022584" "ENSMUSG00000024140" "ENSMUSG00000040584"
## [7] "ENSMUSG00000041378" "ENSMUSG00000030237" "ENSMUSG00000056492"
## [10] "ENSMUSG00000030235" "ENSMUSG00000020154" "ENSMUSG00000029648"
## [13] "ENSMUSG00000033960" "ENSMUSG00000039167" "ENSMUSG00000027199"
## [16] "ENSMUSG00000020717" "ENSMUSG00000037625" "ENSMUSG00000022548"
## [19] "ENSMUSG00000026193" "ENSMUSG00000064373" "ENSMUSG00000026830"
## [22] "ENSMUSG00000027562" "ENSMUSG00000031425" "ENSMUSG00000036634"
## [25] "ENSMUSG00000076439" "ENSMUSG00000032517" "ENSMUSG00000020077"
## [28] "ENSMUSG00000031775" "ENSMUSG00000001946" "ENSMUSG00000015090"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000037625" "ENSMUSG00000026830" "ENSMUSG00000036634"
## [4] "ENSMUSG00000076439" "ENSMUSG00000031425" "ENSMUSG00000032517"
## [7] "ENSMUSG00000050121" "ENSMUSG00000015090" "ENSMUSG00000032060"
## [10] "ENSMUSG00000006782" "ENSMUSG00000027562" "ENSMUSG00000090639"
## [13] "ENSMUSG00000041607" "ENSMUSG00000032854" "ENSMUSG00000022425"
## [16] "ENSMUSG00000020774" "ENSMUSG00000070354" "ENSMUSG00000022548"
## [19] "ENSMUSG00000033579" "ENSMUSG00000046160" "ENSMUSG00000040759"
## [22] "ENSMUSG00000027858" "ENSMUSG00000090996" "ENSMUSG00000073680"
## [25] "ENSMUSG00000047976" "ENSMUSG00000013523" "ENSMUSG00000020486"
## [28] "ENSMUSG00000027199" "ENSMUSG00000037166" "ENSMUSG00000039904"
## [1] ""
## [1] "ENSMUSG00000079018" "ENSMUSG00000075602" "ENSMUSG00000040584"
## [4] "ENSMUSG00000022584" "ENSMUSG00000041378" "ENSMUSG00000056492"
## [7] "ENSMUSG00000030237" "ENSMUSG00000020154" "ENSMUSG00000030235"
## [10] "ENSMUSG00000029648" "ENSMUSG00000039167" "ENSMUSG00000020717"
## [13] "ENSMUSG00000026193" "ENSMUSG00000033960" "ENSMUSG00000001946"
## [16] "ENSMUSG00000024140" "ENSMUSG00000034738" "ENSMUSG00000042745"
## [19] "ENSMUSG00000031239" "ENSMUSG00000020077" "ENSMUSG00000032232"
## [22] "ENSMUSG00000029802" "ENSMUSG00000019966" "ENSMUSG00000006386"
## [25] "ENSMUSG00000039349" "ENSMUSG00000036256" "ENSMUSG00000029484"
## [28] "ENSMUSG00000000530" "ENSMUSG00000026814" "ENSMUSG00000026921"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000022528" "ENSMUSG00000063564" "ENSMUSG00000007682"
## [4] "ENSMUSG00000028195" "ENSMUSG00000045005" "ENSMUSG00000017390"
## [7] "ENSMUSG00000047786" "ENSMUSG00000034640" "ENSMUSG00000029838"
## [10] "ENSMUSG00000022132" "ENSMUSG00000026701" "ENSMUSG00000056380"
## [13] "ENSMUSG00000021250" "ENSMUSG00000018451" "ENSMUSG00000035686"
## [16] "ENSMUSG00000027004" "ENSMUSG00000021732" "ENSMUSG00000033737"
## [19] "ENSMUSG00000044177" "ENSMUSG00000022419" "ENSMUSG00000027447"
## [22] "ENSMUSG00000030428" "ENSMUSG00000000247" "ENSMUSG00000045817"
## [25] "ENSMUSG00000000567" "ENSMUSG00000003545" "ENSMUSG00000049929"
## [28] "ENSMUSG00000005089" "ENSMUSG00000030905" "ENSMUSG00000058897"
## [1] ""
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000094800"
## [4] "ENSMUSG00000038370" "ENSMUSG00000027800" "ENSMUSG00000044772"
## [7] "ENSMUSG00000032595" "ENSMUSG00000072674" "ENSMUSG00000027744"
## [10] "ENSMUSG00000020473" "ENSMUSG00000110332" "ENSMUSG00000021270"
## [13] "ENSMUSG00000041323" "ENSMUSG00000026683" "ENSMUSG00000047139"
## [16] "ENSMUSG00000108841" "ENSMUSG00000021950" "ENSMUSG00000095304"
## [19] "ENSMUSG00000045655" "ENSMUSG00000044475" "ENSMUSG00000026301"
## [22] "ENSMUSG00000072473" "ENSMUSG00000022449" "ENSMUSG00000046242"
## [25] "ENSMUSG00000096054" "ENSMUSG00000047394" "ENSMUSG00000033208"
## [28] "ENSMUSG00000045954" "ENSMUSG00000091345" "ENSMUSG00000037926"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000018451" "ENSMUSG00000029838" "ENSMUSG00000101111"
## [4] "ENSMUSG00000068923" "ENSMUSG00000064341" "ENSMUSG00000065947"
## [7] "ENSMUSG00000040785" "ENSMUSG00000064370" "ENSMUSG00000031633"
## [10] "ENSMUSG00000020315" "ENSMUSG00000064356" "ENSMUSG00000064367"
## [13] "ENSMUSG00000030654" "ENSMUSG00000022892" "ENSMUSG00000021087"
## [16] "ENSMUSG00000055430" "ENSMUSG00000100862" "ENSMUSG00000030428"
## [19] "ENSMUSG00000055254" "ENSMUSG00000079037" "ENSMUSG00000043384"
## [22] "ENSMUSG00000052727" "ENSMUSG00000015222" "ENSMUSG00000045092"
## [25] "ENSMUSG00000056492" "ENSMUSG00000005125" "ENSMUSG00000032766"
## [28] "ENSMUSG00000034723" "ENSMUSG00000017390" "ENSMUSG00000063564"
## [1] ""
## [1] "ENSMUSG00000038642" "ENSMUSG00000036905" "ENSMUSG00000036896"
## [4] "ENSMUSG00000024621" "ENSMUSG00000036887" "ENSMUSG00000030579"
## [7] "ENSMUSG00000058715" "ENSMUSG00000021423" "ENSMUSG00000028581"
## [10] "ENSMUSG00000023992" "ENSMUSG00000046805" "ENSMUSG00000036353"
## [13] "ENSMUSG00000000982" "ENSMUSG00000089929" "ENSMUSG00000015852"
## [16] "ENSMUSG00000021665" "ENSMUSG00000024397" "ENSMUSG00000018008"
## [19] "ENSMUSG00000018930" "ENSMUSG00000026395" "ENSMUSG00000040552"
## [22] "ENSMUSG00000074622" "ENSMUSG00000069516" "ENSMUSG00000024401"
## [25] "ENSMUSG00000040229" "ENSMUSG00000022952" "ENSMUSG00000059498"
## [28] "ENSMUSG00000032691" "ENSMUSG00000030786" "ENSMUSG00000048163"
## [1] ""
## [1] ""Determine statistically significant principal components
## Perform jackstraw statistical test to investigate statistically significant PC.
seurat_campbell_fasted <- JackStraw(object = seurat_campbell_fasted,
num.replicate = 100,
display.progress = TRUE
)##
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## Time Elapsed: 2.14932174285253 mins# Maximum number of PCs allowed = 20.
## Visualise JackStraw plots
JackStrawPlot(object = seurat_campbell_fasted, PCs = 1:20)## Warning: Removed 19616 rows containing missing values (geom_point).
## An object of class seurat in project CAMPBELL_FASTED
## 21789 genes across 3640 samples.## A less computationally intensive heuristic method for finding the statistically significant PCAs is using an elbow plot
PCElbowPlot(object = seurat_campbell_fasted)
Cell clustering
## Cluster cells by PC
seurat_campbell_fasted <- FindClusters(object = seurat_campbell_fasted, reduction.type = "pca", dims.use = 1:20, resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_fasted)## Parameters used in latest FindClusters calculation run on: 2018-11-20 22:43:07
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_fasted <- RunTSNE(object = seurat_campbell_fasted, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_fasted)
TSNEPlot(object = seurat_campbell_fasted,
no.legend = TRUE,
do.label = TRUE)
## 1379 variable genes = 16 clusters
## 1379 variable genes, 10 PC = 13 clusters
## 3006 variable genes, 20 PC = 16 clusters
table(seurat_campbell_fasted@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1091 451 376 335 252 242 188 184 134 132 64 47 40 38 33
## 15
## 33proportion_table<- table(seurat_campbell_fasted@ident, seurat_campbell_fasted@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164441 SRR5164444 SRR5164446
## 0 0.33306 0.21617 0.25075
## 1 0.09480 0.18629 0.17568
## 2 0.08940 0.10896 0.14865
## 3 0.11185 0.06854 0.04054
## 4 0.07401 0.06151 0.05856
## 5 0.07069 0.10018 0.02252
## 6 0.04865 0.08436 0.03453
## 7 0.04200 0.08084 0.05556
## 8 0.03035 0.01406 0.07958
## 9 0.03493 0.04042 0.03754
## 10 0.02412 0.00000 0.00901
## 11 0.01331 0.02109 0.00450
## 12 0.00457 0.00527 0.03904
## 13 0.01414 0.00703 0.00000
## 14 0.01081 0.00527 0.00601
## 15 0.00333 0.00000 0.03754TSNEPlot(object = seurat_campbell_fasted,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = ncol(proportion_table)))
colnames(proportion_table)<- colnames(table(seurat_campbell_fasted@ident,
seurat_campbell_fasted@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_fasted <- FindClusters(object = seurat_campbell_fasted, reduction.type = "pca", dims.use = 1:20,
resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_fasted)## Parameters used in latest FindClusters calculation run on: 2018-11-20 22:43:07
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_fasted <- RunTSNE(object = seurat_campbell_fasted, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_fasted)
TSNEPlot(object = seurat_campbell_fasted,
no.legend = TRUE,
do.label = TRUE)
## 1379 variable genes = 19 clusters
## 1379 variable genes, 10 PC = 15 clusters
## 3006 variable genes, 20 PC = 18 clusters
table(seurat_campbell_fasted@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 572 493 450 337 276 270 249 184 177 134 106 98 64 47 43 38 35 34
## 18
## 33proportion_table<- table(seurat_campbell_fasted@ident, seurat_campbell_fasted@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164441 SRR5164444 SRR5164446
## 0 0.16674 0.10018 0.17117
## 1 0.15967 0.11072 0.06907
## 2 0.09439 0.18629 0.17568
## 3 0.11185 0.06854 0.04354
## 4 0.05904 0.10545 0.11111
## 5 0.07900 0.06503 0.06456
## 6 0.07277 0.10193 0.02402
## 7 0.04699 0.08436 0.03453
## 8 0.03992 0.07733 0.05556
## 9 0.03035 0.01406 0.07958
## 10 0.03243 0.00703 0.03604
## 11 0.02495 0.02636 0.03453
## 12 0.02412 0.00000 0.00901
## 13 0.01331 0.02109 0.00450
## 14 0.00582 0.00527 0.03904
## 15 0.01414 0.00703 0.00000
## 16 0.00998 0.01406 0.00450
## 17 0.01123 0.00527 0.00601
## 18 0.00333 0.00000 0.03754TSNEPlot(object = seurat_campbell_fasted,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = ncol(proportion_table)))
colnames(proportion_table)<- colnames(table(seurat_campbell_fasted@ident,
seurat_campbell_fasted@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_fasted <- FindClusters(object = seurat_campbell_fasted, reduction.type = "pca", dims.use = 1:20,
resolution = 1.5, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_fasted)## Parameters used in latest FindClusters calculation run on: 2018-11-20 22:43:07
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_fasted <- RunTSNE(object = seurat_campbell_fasted, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_fasted)
TSNEPlot(object = seurat_campbell_fasted,
no.legend = TRUE,
do.label = TRUE)
## 1379 variable genes = 20 clusters
## 1379 variable genes, 10 PC = 19 clusters
## 3006 variable genes, 20 PC = 20 clusters
table(seurat_campbell_fasted@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 490 464 449 337 271 269 252 189 184 134 106 102 99 64 47 43 38 35
## 18 19
## 34 33proportion_table<- table(seurat_campbell_fasted@ident, seurat_campbell_fasted@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164441 SRR5164444 SRR5164446
## 0 0.15925 0.10721 0.06907
## 1 0.15551 0.07557 0.07057
## 2 0.09439 0.18453 0.17568
## 3 0.11185 0.06854 0.04354
## 4 0.07942 0.06503 0.06456
## 5 0.05696 0.10193 0.11111
## 6 0.07401 0.10193 0.02402
## 7 0.04823 0.08612 0.03604
## 8 0.04200 0.08084 0.05556
## 9 0.03035 0.01406 0.07958
## 10 0.03243 0.00703 0.03604
## 11 0.00832 0.02812 0.09910
## 12 0.02536 0.02636 0.03453
## 13 0.02412 0.00000 0.00901
## 14 0.01331 0.02109 0.00450
## 15 0.00582 0.00527 0.03904
## 16 0.01414 0.00703 0.00000
## 17 0.00998 0.01406 0.00450
## 18 0.01123 0.00527 0.00601
## 19 0.00333 0.00000 0.03754TSNEPlot(object = seurat_campbell_fasted,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4
)
proportion_table<- data.frame(matrix(proportion_table, ncol = ncol(proportion_table)))
colnames(proportion_table)<- colnames(table(seurat_campbell_fasted@ident,
seurat_campbell_fasted@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
#######
seurat_campbell_fasted <- FindClusters(object = seurat_campbell_fasted, reduction.type = "pca", dims.use = 1:20,
resolution = 2.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_fasted)## Parameters used in latest FindClusters calculation run on: 2018-11-20 22:43:07
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20seurat_campbell_fasted <- RunTSNE(object = seurat_campbell_fasted, dims.use = 1:20, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_fasted)
TSNEPlot(object = seurat_campbell_fasted,
no.legend = TRUE,
do.label = TRUE)
## 1379 variable genes = 23 clusters
## 1379 variable genes, 10 PC = 21 clusters
## 3006 variable genes, 20 PC = 21 clusters
table(seurat_campbell_fasted@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 476 431 258 253 251 249 232 219 189 177 134 125 119 97 94 64 47 45
## 18 19 20 21 22
## 40 38 35 34 33proportion_table<- table(seurat_campbell_fasted@ident, seurat_campbell_fasted@meta.data$replicate_name)
proportion_table<- round(prop.table(proportion_table, 2), 5)
proportion_table##
## SRR5164441 SRR5164444 SRR5164446
## 0 0.15593 0.10369 0.06306
## 1 0.14761 0.06327 0.06006
## 2 0.05447 0.10193 0.10360
## 3 0.07443 0.10193 0.02402
## 4 0.07110 0.06678 0.06306
## 5 0.08690 0.03339 0.03153
## 6 0.08690 0.02460 0.01351
## 7 0.00748 0.16169 0.16366
## 8 0.04823 0.08612 0.03604
## 9 0.03992 0.07733 0.05556
## 10 0.03035 0.01406 0.07958
## 11 0.03701 0.01054 0.04505
## 12 0.01247 0.03339 0.10511
## 13 0.02495 0.02636 0.03303
## 14 0.02661 0.03691 0.01351
## 15 0.02412 0.00000 0.00901
## 16 0.01331 0.02109 0.00450
## 17 0.00624 0.00527 0.04054
## 18 0.01331 0.00527 0.00751
## 19 0.01414 0.00703 0.00000
## 20 0.00998 0.01406 0.00450
## 21 0.01123 0.00527 0.00601
## 22 0.00333 0.00000 0.03754TSNEPlot(object = seurat_campbell_fasted,
group.by = "replicate_name",
no.legend = FALSE,
do.label = FALSE,
pt.size = 0.4 )
proportion_table<- data.frame(matrix(proportion_table, ncol = ncol(proportion_table)))
colnames(proportion_table)<- colnames(table(seurat_campbell_fasted@ident,
seurat_campbell_fasted@meta.data$replicate_name))
chart.Correlation(data.frame( proportion_table[1:5,]) )
# cor(proportion_table[1],proportion_table[4])
## Cluster cells using final parameters (1817 genes, 20 PC, resolution = 0.6)
seurat_campbell_fasted <- FindClusters(object = seurat_campbell_fasted, reduction.type = "pca", dims.use = 1:20, resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
## Produce t-SNE of final parameters:
seurat_campbell_fasted <- RunTSNE(object = seurat_campbell_fasted, dims.use = 1:20, do.fast = TRUE)Use 1379 variable genes and 20 principal components with a resolution of 0.6. This gives a total of 16 clusters.
Finding differentially expressed genes between cell clusters (cluster biomarkers)
## Find markers for every cluster compared to all remaining cells, report both positive and negative genes.
seurat_campbell_fasted_biomarkers <- FindAllMarkers(object = seurat_campbell_fasted, only.pos = FALSE, min.pct = 0.2)
## Get the top 10 biomarkers per cluster
top10_seurat_campbell_markers<- seurat_campbell_fasted_biomarkers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers## # A tibble: 160 x 7
## # Groups: cluster [16]
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 5.87e-131 0.628 0.976 0.795 1.28e-126 0 ENSMUSG00000055430
## 2 5.43e- 86 0.787 0.7 0.37 1.18e- 81 0 ENSMUSG00000035864
## 3 3.11e- 55 0.645 0.423 0.179 6.77e- 51 0 ENSMUSG00000054459
## 4 7.29e- 54 0.613 0.602 0.353 1.59e- 49 0 ENSMUSG00000021700
## 5 1.01e- 53 0.605 0.611 0.364 2.20e- 49 0 ENSMUSG00000066392
## 6 4.19e- 51 0.698 0.384 0.16 9.12e- 47 0 ENSMUSG00000010803
## 7 4.83e- 51 0.836 0.555 0.341 1.05e- 46 0 ENSMUSG00000026787
## 8 6.40e- 50 0.676 0.313 0.113 1.39e- 45 0 ENSMUSG00000037771
## 9 2.58e- 49 0.630 0.511 0.276 5.62e- 45 0 ENSMUSG00000048978
## 10 4.72e- 41 0.770 0.239 0.082 1.03e- 36 0 ENSMUSG00000036357
## # ... with 150 more rows# write.csv(as.data.frame(seurat_campbell_fasted_biomarkers), file = "seurat_campbell_fasted_biomarkers.csv", quote = FALSE)
# write.csv(as.data.frame(top10_seurat_campbell_markers), file = "top10_seurat_campbell_fasted_biomarkers.csv", quote = FALSE)
## Perform ROC DE test. This can take a long time.
seurat_campbell_fasted_biomarkers_ROC <- FindAllMarkers(object = seurat_campbell_fasted, only.pos = FALSE, min.pct = 0.2, test.use = "roc")
top10_seurat_campbell_markers_ROC<- seurat_campbell_fasted_biomarkers_ROC %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers_ROC## # A tibble: 142 x 9
## # Groups: cluster [16]
## myAUC avg_diff power avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0.754 0.628 0.508 0.628 0.976 0.795 NA 0 ENSMUSG00…
## 2 0.728 0.515 0.456 0.515 0.99 0.825 NA 0 ENSMUSG00…
## 3 0.994 4.12 0.988 4.12 0.991 0.105 NA 1 ENSMUSG00…
## 4 0.988 4.86 0.976 4.86 0.98 0.143 NA 1 ENSMUSG00…
## 5 0.845 0.995 0.69 0.995 0.976 0.761 NA 1 ENSMUSG00…
## 6 0.818 1.63 0.636 1.63 0.725 0.137 NA 1 ENSMUSG00…
## 7 0.772 1.66 0.544 1.66 0.561 0.023 NA 1 ENSMUSG00…
## 8 0.736 1.30 0.472 1.30 0.563 0.12 NA 1 ENSMUSG00…
## 9 0.732 1.35 0.464 1.35 0.499 0.041 NA 1 ENSMUSG00…
## 10 0.725 1.40 0.450 1.40 0.49 0.049 NA 1 ENSMUSG00…
## # ... with 132 more rows# write.csv(as.data.frame(seurat_campbell_fasted_biomarkers_ROC), file = "seurat_campbell_fasted_biomarkers_ROC.csv", quote = FALSE)
# write.csv(as.data.frame(top10_seurat_campbell_markers_ROC), file = "top10_seurat_campbell_fasted_biomarkers_ROC.csv", quote = FALSE)
## Plot heatmap of top 10 DE genes
DoHeatmap(object = seurat_campbell_fasted,
genes.use = top10_seurat_campbell_markers$gene,
slim.col.label = TRUE,
remove.key = TRUE)
Save seurat object of chow mice.
## save seurat object as .rds
# saveRDS(seurat_campbell_fasted, file = "./seurat_campbell_fasted_final.rds")Analysis of Campbell scRNAseq data for mice on a LOW FAT DIET(1 replicate) using seurat
Load LFD mouse data
## Load seurat object
seurat_campbell_lfd<- readRDS(file = "./seurat_campbell_lfd_just_created.rds")
seurat_campbell_lfd## An object of class seurat in project CAMPBELL_LFD
## 23273 genes across 3347 samples.Quality control on CHOW mouse data
## Import list of mouse mitocondrially encoded genes:
mito_genes<- read.csv("mito_genes_table.csv", header = TRUE)
## Make list of mitocondrial gene IDS:
mito_genes <- mito_genes$id_with_url
## Identify which mitochondrially expressed gene IDs are present in my dataset:
mito_genes_present<-seurat_campbell_lfd@raw.data[mito_genes, ]
mito_genes_present<- mito_genes[!grepl("NA", rownames(mito_genes_present))]
seurat_campbell_lfd@raw.data[mito_genes_present, 1:5]## AAAAAAGATACT AAAACGAGTACC AAAACTAAAACA AAAAGGTGGGTC
## ENSMUSG00000000028 0 0 0 0
## ENSMUSG00000000037 0 0 0 0
## ENSMUSG00000000149 0 0 0 0
## ENSMUSG00000000148 0 1 0 0
## ENSMUSG00000000202 0 0 0 0
## ENSMUSG00000000154 0 0 0 0
## ENSMUSG00000000134 0 0 0 0
## ENSMUSG00000000126 0 0 0 0
## ENSMUSG00000000085 0 0 0 0
## ENSMUSG00000000194 0 0 0 0
## ENSMUSG00000000093 0 0 0 0
## ENSMUSG00000000167 0 0 0 0
## ENSMUSG00000000094 0 0 0 0
## ENSMUSG00000000131 0 0 0 0
## ENSMUSG00000000120 0 0 0 0
## ENSMUSG00000000197 0 0 0 0
## ENSMUSG00000000058 0 0 0 0
## ENSMUSG00000000159 4 15 0 0
## ENSMUSG00000000168 0 0 0 0
## ENSMUSG00000000056 1 0 0 0
## ENSMUSG00000000049 0 0 0 0
## ENSMUSG00000000184 1 1 0 1
## ENSMUSG00000000142 0 0 0 0
## ENSMUSG00000000078 0 1 0 0
## ENSMUSG00000000001 0 0 0 1
## ENSMUSG00000000223 0 0 0 0
## ENSMUSG00000000127 0 0 0 0
## ENSMUSG00000000171 4 1 0 0
## ENSMUSG00000000214 0 0 0 0
## ENSMUSG00000000088 1 0 0 1
## ENSMUSG00000000125 0 0 0 0
## ENSMUSG00000000031 0 0 0 0
## AAAATCAGCTTC
## ENSMUSG00000000028 0
## ENSMUSG00000000037 0
## ENSMUSG00000000149 0
## ENSMUSG00000000148 0
## ENSMUSG00000000202 0
## ENSMUSG00000000154 0
## ENSMUSG00000000134 0
## ENSMUSG00000000126 0
## ENSMUSG00000000085 0
## ENSMUSG00000000194 0
## ENSMUSG00000000093 0
## ENSMUSG00000000167 0
## ENSMUSG00000000094 0
## ENSMUSG00000000131 0
## ENSMUSG00000000120 0
## ENSMUSG00000000197 0
## ENSMUSG00000000058 0
## ENSMUSG00000000159 4
## ENSMUSG00000000168 0
## ENSMUSG00000000056 0
## ENSMUSG00000000049 0
## ENSMUSG00000000184 0
## ENSMUSG00000000142 0
## ENSMUSG00000000078 0
## ENSMUSG00000000001 0
## ENSMUSG00000000223 0
## ENSMUSG00000000127 0
## ENSMUSG00000000171 1
## ENSMUSG00000000214 0
## ENSMUSG00000000088 0
## ENSMUSG00000000125 0
## ENSMUSG00000000031 0dim(seurat_campbell_lfd@raw.data[mito_genes_present, ])## [1] 32 3347## Calculate the percentage of mitcondrial gene counts per cell
percent_mito <- Matrix::colSums(seurat_campbell_lfd@raw.data[mito_genes_present, ])/Matrix::colSums(seurat_campbell_lfd@raw.data)
## Basic stats of proportion of mitocondrial gene expression per cell
summary(percent_mito)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0007315 0.0012928 0.0015295 0.0020677 0.0076620## add percentage mitocondrial genes into metadata
seurat_campbell_lfd <- AddMetaData(object = seurat_campbell_lfd,
metadata = percent_mito,
col.name = "percent_mito")
## Look at the seurat object meta data
head(seurat_campbell_lfd@meta.data)## nGene nUMI orig.ident replicate_name percent_mito
## AAAAAAGATACT 2806 5638 CAMPBELL_LFD SRR5164439 0.0019517388
## AAAACGAGTACC 3030 7629 CAMPBELL_LFD SRR5164439 0.0024924570
## AAAACTAAAACA 964 1996 CAMPBELL_LFD SRR5164439 0.0000000000
## AAAAGGTGGGTC 2747 6120 CAMPBELL_LFD SRR5164439 0.0004903563
## AAAATCAGCTTC 2886 5655 CAMPBELL_LFD SRR5164439 0.0008849558
## AAAATGAGACGG 1236 1768 CAMPBELL_LFD SRR5164439 0.0011312217## QC plots of number of genes, UMIs, and % mitochondria
VlnPlot(object = seurat_campbell_lfd,
features.plot = c("nGene", "nUMI", "percent_mito"),
nCol = 3,
x.lab.rot = TRUE,
point.size.use = 0.2
)
## QC plots to show the relationship between nUMIs and relative mitocondrial gene expression or number of genes.
par(mfrow = c(1, 2))
# GenePlot(object = seurat_campbell_lfd, gene1 = "nUMI", gene2 = "percent_mito", do.hover = TRUE, pch.use = 1)
# GenePlot(object = seurat_campbell_lfd, gene1 = "nUMI", gene2 = "nGene", do.hover = TRUE, pch.use = 1)
GenePlot(object = seurat_campbell_lfd, gene1 = "nUMI", gene2 = "percent_mito", pch.use = 16, cex.use = 0.5)
GenePlot(object = seurat_campbell_lfd, gene1 = "nUMI", gene2 = "nGene", pch.use = 16, cex.use = 0.5)
Filter cells after QC
Filter cells out with more than 0.4% of total gene expression comming from mitocondrially encoded genes and more than 5000 genes expressed.
## manual check; I already know all cells have >800 genes
table(seurat_campbell_lfd@meta.data$percent_mito < 0.004 & seurat_campbell_lfd@meta.data$nGene<5000)##
## FALSE TRUE
## 213 3134# FALSE TRUE
# 213 3134
## Filter cells with <0.4% percent_mito and <5000 genes
seurat_campbell_lfd <- FilterCells(object = seurat_campbell_lfd,
subset.names = c("nGene", "percent_mito"),
low.thresholds = c(800, -Inf),
high.thresholds = c(5000, 0.004))
seurat_campbell_lfd## An object of class seurat in project CAMPBELL_LFD
## 23273 genes across 3134 samples.# An object of class seurat in project CAMPBELL_FASTED
# 21789 genes across 3640 samples.Log normalise gene expression per cell
## Plot graph of total expression before normalisation
hist(colSums(seurat_campbell_lfd@data),
breaks = 100,
main = "Total expression before normalisation",
xlab = "Sum of expression")
## Normalise gene expression per cell
seurat_campbell_lfd <- NormalizeData(object = seurat_campbell_lfd, normalization.method = "LogNormalize", scale.factor = 10000)
## Plot graph of total expression after normalisation
hist(colSums(as.data.frame(as.matrix(seurat_campbell_lfd@data))),
breaks = 100,
main = "Total expression after normalisation",
xlab = "Sum of expression")
Find genes whose expression varies between cells.
Find genes whose expression varies between cells, which will be used to construct principal componets between cells that will be used for clustering.
## Find variable genes by expression
seurat_campbell_lfd <- FindVariableGenes(object = seurat_campbell_lfd,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.05,
x.high.cutoff = 3,
y.cutoff = 0.75,
num.bin = 20,
binning.method = "equal_width"
)
# number of variable genes
length(seurat_campbell_lfd@var.genes)## [1] 1480seurat_campbell_lfd <- FindVariableGenes(object = seurat_campbell_lfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1484 variable genesseurat_campbell_lfd <- FindVariableGenes(object = seurat_campbell_lfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 4, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 2420 variable genesseurat_campbell_lfd <- FindVariableGenes(object = seurat_campbell_lfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1986 variable genesseurat_campbell_lfd <- FindVariableGenes(object = seurat_campbell_lfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 3189 variable genesseurat_campbell_lfd <- FindVariableGenes(object = seurat_campbell_lfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 40, binning.method = “equal_width” )
== 3122 variable geneseurat_campbell_lfd <- FindVariableGenes(object = seurat_campbell_lfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 3, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1480 variable gene
Scale normalised gene expression per cell to remove unwanted sources of variation
Scale gene expression per cell by building linear models for nUMI, percent_mito and mouse replicate.
## Scale data nUMI, percent_mito and mouse replicate
seurat_campbell_lfd <- ScaleData(object = seurat_campbell_lfd, vars.to.regress = c("nUMI", "percent_mito"))## Regressing out: nUMI, percent_mito##
## Time Elapsed: 29.3174724578857 secs## Scaling data matrixPrincipal component anlysis of variable genes.
Principal component anlysis of variable genes for use in cell clustering.
## Perform principal component analysis on variable genes
seurat_campbell_lfd <- RunPCA(object = seurat_campbell_lfd,
pc.genes = seurat_campbell_lfd@var.genes,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 5)## [1] "PC1"
## [1] "ENSMUSG00000050711" "ENSMUSG00000022577" "ENSMUSG00000043388"
## [4] "ENSMUSG00000036699" "ENSMUSG00000044349"
## [1] ""
## [1] "ENSMUSG00000031765" "ENSMUSG00000031762" "ENSMUSG00000026701"
## [4] "ENSMUSG00000001025" "ENSMUSG00000000567"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000037852" "ENSMUSG00000055254" "ENSMUSG00000026701"
## [4] "ENSMUSG00000017390" "ENSMUSG00000035805"
## [1] ""
## [1] "ENSMUSG00000020077" "ENSMUSG00000079018" "ENSMUSG00000075602"
## [4] "ENSMUSG00000041378" "ENSMUSG00000040584"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000058715" "ENSMUSG00000036896" "ENSMUSG00000036887"
## [4] "ENSMUSG00000036905" "ENSMUSG00000030579"
## [1] ""
## [1] "ENSMUSG00000037625" "ENSMUSG00000031425" "ENSMUSG00000076439"
## [4] "ENSMUSG00000026830" "ENSMUSG00000032854"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000079018" "ENSMUSG00000040584" "ENSMUSG00000041378"
## [4] "ENSMUSG00000056492" "ENSMUSG00000075602"
## [1] ""
## [1] "ENSMUSG00000036896" "ENSMUSG00000036905" "ENSMUSG00000036887"
## [4] "ENSMUSG00000058715" "ENSMUSG00000024621"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000026830" "ENSMUSG00000050121" "ENSMUSG00000015090"
## [4] "ENSMUSG00000076439" "ENSMUSG00000027375"
## [1] ""
## [1] "ENSMUSG00000031610" "ENSMUSG00000026249" "ENSMUSG00000026424"
## [4] "ENSMUSG00000029231" "ENSMUSG00000019874"
## [1] ""
## [1] ""## visualise top genes associated with principal components
VizPCA(object = seurat_campbell_lfd, pcs.use = 1:9)
## Plot principal component 1 v's 2
PCAPlot(object = seurat_campbell_lfd, dim.1 = 1, dim.2 = 2)
## Plot principal component 2 v's 3
PCAPlot(object = seurat_campbell_lfd, dim.1 = 2, dim.2 = 3)
## Plot heat map for gene expression of principal component 1 genes
PCHeatmap(object = seurat_campbell_lfd, pc.use = 1, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 2 genes
PCHeatmap(object = seurat_campbell_lfd, pc.use = 2, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 3 genes
PCHeatmap(object = seurat_campbell_lfd, pc.use = 3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 4 genes
PCHeatmap(object = seurat_campbell_lfd, pc.use = 4, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot multiple heatmaps of gene expression per PC genes
PCHeatmap(object = seurat_campbell_lfd,
pc.use = 5:18,
cells.use = 500,
do.balanced = TRUE,
label.columns = FALSE)
## Takes a pre-computed PCA (from most variable genes identified earlier) and projects this onto the entire dataset (all genes)
seurat_campbell_lfd <- ProjectPCA(object = seurat_campbell_lfd, do.print = TRUE)## [1] "PC1"
## [1] "ENSMUSG00000033061" "ENSMUSG00000047261" "ENSMUSG00000027581"
## [4] "ENSMUSG00000050711" "ENSMUSG00000025468" "ENSMUSG00000024268"
## [7] "ENSMUSG00000022577" "ENSMUSG00000043388" "ENSMUSG00000055430"
## [10] "ENSMUSG00000036699" "ENSMUSG00000021087" "ENSMUSG00000029223"
## [13] "ENSMUSG00000019986" "ENSMUSG00000044349" "ENSMUSG00000026576"
## [16] "ENSMUSG00000027350" "ENSMUSG00000027500" "ENSMUSG00000019923"
## [19] "ENSMUSG00000035964" "ENSMUSG00000059361" "ENSMUSG00000042750"
## [22] "ENSMUSG00000031840" "ENSMUSG00000018965" "ENSMUSG00000060188"
## [25] "ENSMUSG00000071658" "ENSMUSG00000027273" "ENSMUSG00000066705"
## [28] "ENSMUSG00000040785" "ENSMUSG00000022658" "ENSMUSG00000020297"
## [1] ""
## [1] "ENSMUSG00000002985" "ENSMUSG00000026385" "ENSMUSG00000031765"
## [4] "ENSMUSG00000031762" "ENSMUSG00000026701" "ENSMUSG00000001025"
## [7] "ENSMUSG00000000567" "ENSMUSG00000026728" "ENSMUSG00000067786"
## [10] "ENSMUSG00000035805" "ENSMUSG00000058135" "ENSMUSG00000022528"
## [13] "ENSMUSG00000029838" "ENSMUSG00000050953" "ENSMUSG00000018593"
## [16] "ENSMUSG00000005360" "ENSMUSG00000095538" "ENSMUSG00000055254"
## [19] "ENSMUSG00000027712" "ENSMUSG00000044080" "ENSMUSG00000018102"
## [22] "ENSMUSG00000008540" "ENSMUSG00000027447" "ENSMUSG00000032231"
## [25] "ENSMUSG00000036570" "ENSMUSG00000034467" "ENSMUSG00000063564"
## [28] "ENSMUSG00000031342" "ENSMUSG00000008575" "ENSMUSG00000017390"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000018451" "ENSMUSG00000037852" "ENSMUSG00000067786"
## [4] "ENSMUSG00000026223" "ENSMUSG00000055254" "ENSMUSG00000101111"
## [7] "ENSMUSG00000052727" "ENSMUSG00000039278" "ENSMUSG00000031760"
## [10] "ENSMUSG00000026701" "ENSMUSG00000058254" "ENSMUSG00000031428"
## [13] "ENSMUSG00000064370" "ENSMUSG00000017390" "ENSMUSG00000021270"
## [16] "ENSMUSG00000046432" "ENSMUSG00000116358" "ENSMUSG00000021268"
## [19] "ENSMUSG00000035805" "ENSMUSG00000021379" "ENSMUSG00000032324"
## [22] "ENSMUSG00000025666" "ENSMUSG00000001025" "ENSMUSG00000006373"
## [25] "ENSMUSG00000050071" "ENSMUSG00000079037" "ENSMUSG00000000567"
## [28] "ENSMUSG00000019505" "ENSMUSG00000044550" "ENSMUSG00000030647"
## [1] ""
## [1] "ENSMUSG00000020077" "ENSMUSG00000079018" "ENSMUSG00000075602"
## [4] "ENSMUSG00000041378" "ENSMUSG00000040584" "ENSMUSG00000036896"
## [7] "ENSMUSG00000022584" "ENSMUSG00000016494" "ENSMUSG00000058715"
## [10] "ENSMUSG00000036887" "ENSMUSG00000036905" "ENSMUSG00000056492"
## [13] "ENSMUSG00000030235" "ENSMUSG00000028581" "ENSMUSG00000029484"
## [16] "ENSMUSG00000030237" "ENSMUSG00000024621" "ENSMUSG00000030579"
## [19] "ENSMUSG00000021423" "ENSMUSG00000020154" "ENSMUSG00000023992"
## [22] "ENSMUSG00000024397" "ENSMUSG00000033960" "ENSMUSG00000029622"
## [25] "ENSMUSG00000036256" "ENSMUSG00000038642" "ENSMUSG00000001123"
## [28] "ENSMUSG00000015852" "ENSMUSG00000000530" "ENSMUSG00000060802"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000058715" "ENSMUSG00000036896" "ENSMUSG00000036887"
## [4] "ENSMUSG00000036905" "ENSMUSG00000030579" "ENSMUSG00000028581"
## [7] "ENSMUSG00000023992" "ENSMUSG00000024621" "ENSMUSG00000021423"
## [10] "ENSMUSG00000024397" "ENSMUSG00000015852" "ENSMUSG00000038642"
## [13] "ENSMUSG00000046805" "ENSMUSG00000048163" "ENSMUSG00000027447"
## [16] "ENSMUSG00000036353" "ENSMUSG00000000682" "ENSMUSG00000059498"
## [19] "ENSMUSG00000040229" "ENSMUSG00000030786" "ENSMUSG00000052160"
## [22] "ENSMUSG00000040747" "ENSMUSG00000054675" "ENSMUSG00000030844"
## [25] "ENSMUSG00000018008" "ENSMUSG00000020377" "ENSMUSG00000026126"
## [28] "ENSMUSG00000044811" "ENSMUSG00000069516" "ENSMUSG00000018451"
## [1] ""
## [1] "ENSMUSG00000037625" "ENSMUSG00000031425" "ENSMUSG00000076439"
## [4] "ENSMUSG00000026830" "ENSMUSG00000032854" "ENSMUSG00000027375"
## [7] "ENSMUSG00000036634" "ENSMUSG00000050121" "ENSMUSG00000032517"
## [10] "ENSMUSG00000031775" "ENSMUSG00000033579" "ENSMUSG00000032060"
## [13] "ENSMUSG00000027562" "ENSMUSG00000046160" "ENSMUSG00000073680"
## [16] "ENSMUSG00000022425" "ENSMUSG00000041607" "ENSMUSG00000037166"
## [19] "ENSMUSG00000006782" "ENSMUSG00000015090" "ENSMUSG00000027858"
## [22] "ENSMUSG00000020486" "ENSMUSG00000040759" "ENSMUSG00000013523"
## [25] "ENSMUSG00000027199" "ENSMUSG00000026888" "ENSMUSG00000022090"
## [28] "ENSMUSG00000043448" "ENSMUSG00000020774" "ENSMUSG00000090996"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000079018" "ENSMUSG00000040584" "ENSMUSG00000041378"
## [4] "ENSMUSG00000056492" "ENSMUSG00000075602" "ENSMUSG00000030235"
## [7] "ENSMUSG00000022584" "ENSMUSG00000030237" "ENSMUSG00000020154"
## [10] "ENSMUSG00000033960" "ENSMUSG00000001946" "ENSMUSG00000029648"
## [13] "ENSMUSG00000036256" "ENSMUSG00000031239" "ENSMUSG00000026193"
## [16] "ENSMUSG00000039167" "ENSMUSG00000025902" "ENSMUSG00000029086"
## [19] "ENSMUSG00000042116" "ENSMUSG00000028776" "ENSMUSG00000034738"
## [22] "ENSMUSG00000061353" "ENSMUSG00000039349" "ENSMUSG00000030413"
## [25] "ENSMUSG00000000530" "ENSMUSG00000006386" "ENSMUSG00000020717"
## [28] "ENSMUSG00000027435" "ENSMUSG00000009687" "ENSMUSG00000045954"
## [1] ""
## [1] "ENSMUSG00000036896" "ENSMUSG00000036905" "ENSMUSG00000036887"
## [4] "ENSMUSG00000058715" "ENSMUSG00000024621" "ENSMUSG00000028581"
## [7] "ENSMUSG00000021423" "ENSMUSG00000023992" "ENSMUSG00000030579"
## [10] "ENSMUSG00000024397" "ENSMUSG00000038642" "ENSMUSG00000015852"
## [13] "ENSMUSG00000048163" "ENSMUSG00000046805" "ENSMUSG00000036353"
## [16] "ENSMUSG00000090639" "ENSMUSG00000052160" "ENSMUSG00000059498"
## [19] "ENSMUSG00000030786" "ENSMUSG00000018008" "ENSMUSG00000000682"
## [22] "ENSMUSG00000040747" "ENSMUSG00000040229" "ENSMUSG00000054675"
## [25] "ENSMUSG00000036908" "ENSMUSG00000020377" "ENSMUSG00000098112"
## [28] "ENSMUSG00000026126" "ENSMUSG00000044811" "ENSMUSG00000070354"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000024661" "ENSMUSG00000026830" "ENSMUSG00000050121"
## [4] "ENSMUSG00000015090" "ENSMUSG00000076439" "ENSMUSG00000027375"
## [7] "ENSMUSG00000022548" "ENSMUSG00000027562" "ENSMUSG00000037625"
## [10] "ENSMUSG00000032517" "ENSMUSG00000036634" "ENSMUSG00000037166"
## [13] "ENSMUSG00000020774" "ENSMUSG00000022090" "ENSMUSG00000020486"
## [16] "ENSMUSG00000026421" "ENSMUSG00000043448" "ENSMUSG00000090639"
## [19] "ENSMUSG00000073680" "ENSMUSG00000064351" "ENSMUSG00000026255"
## [22] "ENSMUSG00000038155" "ENSMUSG00000052727" "ENSMUSG00000101111"
## [25] "ENSMUSG00000041607" "ENSMUSG00000100862" "ENSMUSG00000024810"
## [28] "ENSMUSG00000090996" "ENSMUSG00000083563" "ENSMUSG00000039904"
## [1] ""
## [1] "ENSMUSG00000031610" "ENSMUSG00000026249" "ENSMUSG00000026424"
## [4] "ENSMUSG00000029231" "ENSMUSG00000019874" "ENSMUSG00000106379"
## [7] "ENSMUSG00000067879" "ENSMUSG00000032911" "ENSMUSG00000045532"
## [10] "ENSMUSG00000052229" "ENSMUSG00000079056" "ENSMUSG00000039830"
## [13] "ENSMUSG00000016995" "ENSMUSG00000032482" "ENSMUSG00000033208"
## [16] "ENSMUSG00000069763" "ENSMUSG00000068748" "ENSMUSG00000046160"
## [19] "ENSMUSG00000063297" "ENSMUSG00000017386" "ENSMUSG00000034164"
## [22] "ENSMUSG00000034353" "ENSMUSG00000034000" "ENSMUSG00000028655"
## [25] "ENSMUSG00000022122" "ENSMUSG00000069662" "ENSMUSG00000026955"
## [28] "ENSMUSG00000030317" "ENSMUSG00000006800" "ENSMUSG00000086596"
## [1] ""
## [1] ""Determine statistically significant principal components
## Perform jackstraw statistical test to investigate statistically significant PC.
seurat_campbell_lfd <- JackStraw(object = seurat_campbell_lfd,
num.replicate = 100,
display.progress = TRUE
)##
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## Time Elapsed: 1.89125941197077 mins# Maximum number of PCs allowed = 20.
## Visualise JackStraw plots
JackStrawPlot(object = seurat_campbell_lfd, PCs = 1:20)## Warning: Removed 20911 rows containing missing values (geom_point).
## An object of class seurat in project CAMPBELL_LFD
## 23273 genes across 3134 samples.## A less computationally intensive heuristic method for finding the statistically significant PCAs is using an elbow plot
PCElbowPlot(object = seurat_campbell_lfd)
Cell clustering
## Cluster cells by PC
seurat_campbell_lfd <- FindClusters(object = seurat_campbell_lfd, reduction.type = "pca", dims.use = 1:16, resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_lfd)## Parameters used in latest FindClusters calculation run on: 2018-11-20 23:14:19
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16seurat_campbell_lfd <- RunTSNE(object = seurat_campbell_lfd, dims.use = 1:16, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_lfd)
TSNEPlot(object = seurat_campbell_lfd,
no.legend = TRUE,
do.label = TRUE)
## 1986 variable genes, 20 PC = 14 clusters
## 1986 variable genes, 16 PC = 14 clusters
table(seurat_campbell_lfd@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13
## 639 617 496 347 206 200 122 120 107 89 77 50 35 29#######
seurat_campbell_lfd <- FindClusters(object = seurat_campbell_lfd, reduction.type = "pca", dims.use = 1:16,
resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_lfd)## Parameters used in latest FindClusters calculation run on: 2018-11-20 23:14:19
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16seurat_campbell_lfd <- RunTSNE(object = seurat_campbell_lfd, dims.use = 1:16, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_lfd)
TSNEPlot(object = seurat_campbell_lfd,
no.legend = TRUE,
do.label = TRUE)
## 1986 variable genes, 20 PC = 18 clusters
## 1986 variable genes, 16 PC = 16 clusters
table(seurat_campbell_lfd@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 584 508 491 312 225 206 141 122 113 107 89 86 50 36 35 29#######
seurat_campbell_lfd <- FindClusters(object = seurat_campbell_lfd, reduction.type = "pca", dims.use = 1:16,
resolution = 1.5, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_lfd)## Parameters used in latest FindClusters calculation run on: 2018-11-20 23:14:19
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16seurat_campbell_lfd <- RunTSNE(object = seurat_campbell_lfd, dims.use = 1:16, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_lfd)
TSNEPlot(object = seurat_campbell_lfd,
no.legend = TRUE,
do.label = TRUE)
## 1986 variable genes, 20 PC = 18 clusters
## 1986 variable genes, 16 PC = 18 clusters
table(seurat_campbell_lfd@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 490 429 315 284 240 225 173 170 138 123 113 109 89 86 50 36 35 29#######
seurat_campbell_lfd <- FindClusters(object = seurat_campbell_lfd, reduction.type = "pca", dims.use = 1:16,
resolution = 2.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_lfd)## Parameters used in latest FindClusters calculation run on: 2018-11-20 23:14:19
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16seurat_campbell_lfd <- RunTSNE(object = seurat_campbell_lfd, dims.use = 1:16, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_lfd)
TSNEPlot(object = seurat_campbell_lfd,
no.legend = TRUE,
do.label = TRUE)
## 1986 variable genes, 20 PC = 20 clusters
## 1986 variable genes, 16 PC = 20 clusters
table(seurat_campbell_lfd@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 380 287 255 253 242 224 205 173 168 140 139 123 113 107 89 86 50 36
## 18 19
## 35 29## Cluster cells using final parameters (1986 genes, 16 PC, resolution = 1.0)
seurat_campbell_lfd <- FindClusters(object = seurat_campbell_lfd, reduction.type = "pca", dims.use = 1:16, resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
## Produce t-SNE of final parameters:
seurat_campbell_lfd <- RunTSNE(object = seurat_campbell_lfd, dims.use = 1:16, do.fast = TRUE)Use 1986 variable genes and 16 principal components with a resolution of 1.0 (Changing number of PC makes very little difference to the clustering). This gives a total of 16 clusters.
Finding differentially expressed genes between cell clusters (cluster biomarkers)
## Find markers for every cluster compared to all remaining cells, report both positive and negative genes.
seurat_campbell_lfd_biomarkers <- FindAllMarkers(object = seurat_campbell_lfd, only.pos = FALSE, min.pct = 0.2)
## Get the top 10 biomarkers per cluster
top10_seurat_campbell_markers<- seurat_campbell_lfd_biomarkers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers## # A tibble: 160 x 7
## # Groups: cluster [16]
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 2.19e-149 0.871 0.793 0.218 5.09e-145 0 ENSMUSG00000054459
## 2 1.06e-145 0.895 0.981 0.461 2.46e-141 0 ENSMUSG00000047261
## 3 6.87e-140 0.886 0.916 0.346 1.60e-135 0 ENSMUSG00000027273
## 4 2.39e-131 0.830 0.94 0.409 5.56e-127 0 ENSMUSG00000027500
## 5 2.15e-113 0.890 0.899 0.403 4.99e-109 0 ENSMUSG00000000159
## 6 2.58e-113 0.871 0.786 0.281 6.01e-109 0 ENSMUSG00000035864
## 7 6.48e-107 0.811 0.981 0.727 1.51e-102 0 ENSMUSG00000021268
## 8 1.03e-101 0.871 0.536 0.135 2.41e- 97 0 ENSMUSG00000028222
## 9 8.29e- 23 1.28 0.337 0.159 1.93e- 18 0 ENSMUSG00000020660
## 10 3.17e- 17 1.20 0.267 0.129 7.38e- 13 0 ENSMUSG00000004366
## # ... with 150 more rows# write.csv(as.data.frame(seurat_campbell_lfd_biomarkers), file = "seurat_campbell_lfd_biomarkers.csv", quote = FALSE)
# write.csv(as.data.frame(top10_seurat_campbell_markers), file = "top10_seurat_campbell_lfd_biomarkers.csv", quote = FALSE)
## Perform ROC DE test. This can take a long time.
seurat_campbell_lfd_biomarkers_ROC <- FindAllMarkers(object = seurat_campbell_lfd, only.pos = FALSE, min.pct = 0.2, test.use = "roc")
top10_seurat_campbell_markers_ROC<- seurat_campbell_lfd_biomarkers_ROC %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers_ROC## # A tibble: 160 x 9
## # Groups: cluster [16]
## myAUC avg_diff power avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0.825 0.895 0.650 0.895 0.981 0.461 NA 0 ENSMUSG00…
## 2 0.805 0.886 0.61 0.886 0.916 0.346 NA 0 ENSMUSG00…
## 3 0.803 0.830 0.606 0.830 0.94 0.409 NA 0 ENSMUSG00…
## 4 0.789 0.811 0.578 0.811 0.981 0.727 NA 0 ENSMUSG00…
## 5 0.787 0.871 0.574 0.871 0.793 0.218 NA 0 ENSMUSG00…
## 6 0.78 0.890 0.56 0.890 0.899 0.403 NA 0 ENSMUSG00…
## 7 0.77 0.775 0.54 0.775 0.988 0.823 NA 0 ENSMUSG00…
## 8 0.761 0.871 0.522 0.871 0.786 0.281 NA 0 ENSMUSG00…
## 9 0.708 0.778 0.416 0.778 0.63 0.21 NA 0 ENSMUSG00…
## 10 0.702 0.871 0.404 0.871 0.536 0.135 NA 0 ENSMUSG00…
## # ... with 150 more rows# write.csv(as.data.frame(seurat_campbell_lfd_biomarkers_ROC), file = "seurat_campbell_lfd_biomarkers_ROC.csv", quote = FALSE)
# write.csv(as.data.frame(top10_seurat_campbell_markers_ROC), file = "top10_seurat_campbell_lfd_biomarkers_ROC.csv", quote = FALSE)
## Plot heatmap of top 10 DE genes
DoHeatmap(object = seurat_campbell_lfd,
genes.use = top10_seurat_campbell_markers$gene,
slim.col.label = TRUE,
remove.key = TRUE)
Save seurat object of chow mice.
## save seurat object as .rds
# saveRDS(seurat_campbell_lfd, file = "./seurat_campbell_lfd_final.rds")Analysis of Campbell scRNAseq data for mice on a High FAT DIET (1 replicate) using seurat
Load HFD mouse data
## Load seurat object
seurat_campbell_hfd<- readRDS(file = "./seurat_campbell_hfd_just_created.rds")
seurat_campbell_hfd## An object of class seurat in project CAMPBELL_HFD
## 23685 genes across 3778 samples.Quality control on CHOW mouse data
## Import list of mouse mitocondrially encoded genes:
mito_genes<- read.csv("mito_genes_table.csv", header = TRUE)
## Make list of mitocondrial gene IDS:
mito_genes <- mito_genes$id_with_url
## Identify which mitochondrially expressed gene IDs are present in my dataset:
mito_genes_present<-seurat_campbell_hfd@raw.data[mito_genes, ]
mito_genes_present<- mito_genes[!grepl("NA", rownames(mito_genes_present))]
seurat_campbell_hfd@raw.data[mito_genes_present, 1:5]## AAAAAATGGAGC AAAACGCGTGTC AAAACGGACAGA AAAACGTAACAG
## ENSMUSG00000000028 0 0 0 0
## ENSMUSG00000000037 0 0 0 0
## ENSMUSG00000000154 0 0 0 0
## ENSMUSG00000000149 1 0 0 0
## ENSMUSG00000000202 0 0 0 1
## ENSMUSG00000000159 0 1 5 0
## ENSMUSG00000000142 0 0 0 0
## ENSMUSG00000000127 0 1 0 0
## ENSMUSG00000000085 0 0 1 0
## ENSMUSG00000000194 0 0 0 0
## ENSMUSG00000000093 0 0 0 0
## ENSMUSG00000000168 0 0 0 0
## ENSMUSG00000000120 0 0 0 0
## ENSMUSG00000000134 0 0 0 0
## ENSMUSG00000000125 0 0 0 0
## ENSMUSG00000000197 0 0 0 0
## ENSMUSG00000000058 0 0 0 0
## ENSMUSG00000000167 0 0 0 0
## ENSMUSG00000000171 0 0 1 0
## ENSMUSG00000000056 0 0 0 0
## ENSMUSG00000000049 0 0 0 0
## ENSMUSG00000000184 0 0 1 3
## ENSMUSG00000000148 0 0 0 0
## ENSMUSG00000000078 0 0 0 1
## ENSMUSG00000000001 0 0 0 0
## ENSMUSG00000000223 0 0 0 0
## ENSMUSG00000000131 0 0 0 0
## ENSMUSG00000000183 0 0 0 0
## ENSMUSG00000000214 0 0 0 0
## ENSMUSG00000000088 0 0 1 0
## ENSMUSG00000000126 0 0 0 0
## ENSMUSG00000000031 0 0 0 0
## AAAACTAGTGGT
## ENSMUSG00000000028 0
## ENSMUSG00000000037 0
## ENSMUSG00000000154 0
## ENSMUSG00000000149 0
## ENSMUSG00000000202 0
## ENSMUSG00000000159 0
## ENSMUSG00000000142 0
## ENSMUSG00000000127 0
## ENSMUSG00000000085 0
## ENSMUSG00000000194 1
## ENSMUSG00000000093 0
## ENSMUSG00000000168 0
## ENSMUSG00000000120 0
## ENSMUSG00000000134 0
## ENSMUSG00000000125 0
## ENSMUSG00000000197 0
## ENSMUSG00000000058 0
## ENSMUSG00000000167 0
## ENSMUSG00000000171 0
## ENSMUSG00000000056 0
## ENSMUSG00000000049 0
## ENSMUSG00000000184 0
## ENSMUSG00000000148 0
## ENSMUSG00000000078 0
## ENSMUSG00000000001 1
## ENSMUSG00000000223 0
## ENSMUSG00000000131 0
## ENSMUSG00000000183 0
## ENSMUSG00000000214 0
## ENSMUSG00000000088 0
## ENSMUSG00000000126 0
## ENSMUSG00000000031 0dim(seurat_campbell_hfd@raw.data[mito_genes_present, ])## [1] 32 3778## Calculate the percentage of mitcondrial gene counts per cell
percent_mito <- Matrix::colSums(seurat_campbell_hfd@raw.data[mito_genes_present, ])/Matrix::colSums(seurat_campbell_hfd@raw.data)
## Basic stats of proportion of mitocondrial gene expression per cell
summary(percent_mito)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0008024 0.0014544 0.0017117 0.0023004 0.0085837## add percentage mitocondrial genes into metadata
seurat_campbell_hfd <- AddMetaData(object = seurat_campbell_hfd,
metadata = percent_mito,
col.name = "percent_mito")
## Look at the seurat object meta data
head(seurat_campbell_hfd@meta.data)## nGene nUMI orig.ident replicate_name percent_mito
## AAAAAATGGAGC 921 1362 CAMPBELL_HFD SRR5164440 0.0007342144
## AAAACGCGTGTC 1348 2221 CAMPBELL_HFD SRR5164440 0.0009009009
## AAAACGGACAGA 2127 3652 CAMPBELL_HFD SRR5164440 0.0024671053
## AAAACGTAACAG 1988 3575 CAMPBELL_HFD SRR5164440 0.0013993843
## AAAACTAGTGGT 1723 3175 CAMPBELL_HFD SRR5164440 0.0006301197
## AAAACTATCCCC 3099 6753 CAMPBELL_HFD SRR5164440 0.0029638411## QC plots of number of genes, UMIs, and % mitochondria
VlnPlot(object = seurat_campbell_hfd,
features.plot = c("nGene", "nUMI", "percent_mito"),
nCol = 3,
x.lab.rot = TRUE,
point.size.use = 0.2
)
## QC plots to show the relationship between nUMIs and relative mitocondrial gene expression or number of genes.
par(mfrow = c(1, 2))
# GenePlot(object = seurat_campbell_hfd, gene1 = "nUMI", gene2 = "percent_mito", do.hover = TRUE, pch.use = 1)
# GenePlot(object = seurat_campbell_hfd, gene1 = "nUMI", gene2 = "nGene", do.hover = TRUE, pch.use = 1)
GenePlot(object = seurat_campbell_hfd, gene1 = "nUMI", gene2 = "percent_mito", pch.use = 16, cex.use = 0.5)
GenePlot(object = seurat_campbell_hfd, gene1 = "nUMI", gene2 = "nGene", pch.use = 16, cex.use = 0.5)
Filter cells after QC
Filter cells out with more than 0.4% of total gene expression comming from mitocondrially encoded genes and more than 5000 genes expressed.
## manual check; I already know all cells have >800 genes
table(seurat_campbell_hfd@meta.data$percent_mito < 0.004 & seurat_campbell_hfd@meta.data$nGene<5000)##
## FALSE TRUE
## 296 3482# FALSE TRUE
# 296 3482
## Filter cells with <0.4% percent_mito and <5000 genes
seurat_campbell_hfd <- FilterCells(object = seurat_campbell_hfd,
subset.names = c("nGene", "percent_mito"),
low.thresholds = c(800, -Inf),
high.thresholds = c(5000, 0.004))
seurat_campbell_hfd## An object of class seurat in project CAMPBELL_HFD
## 23685 genes across 3482 samples.# An object of class seurat in project CAMPBELL_HFD
# 23685 genes across 3482 samples.Log normalise gene expression per cell
## Plot graph of total expression before normalisation
hist(colSums(seurat_campbell_hfd@data),
breaks = 100,
main = "Total expression before normalisation",
xlab = "Sum of expression")
## Normalise gene expression per cell
seurat_campbell_hfd <- NormalizeData(object = seurat_campbell_hfd, normalization.method = "LogNormalize", scale.factor = 10000)
## Plot graph of total expression after normalisation
hist(colSums(as.data.frame(as.matrix(seurat_campbell_hfd@data))),
breaks = 100,
main = "Total expression after normalisation",
xlab = "Sum of expression")
Find genes whose expression varies between cells.
Find genes whose expression varies between cells, which will be used to construct principal componets between cells that will be used for clustering.
## Find variable genes by expression
seurat_campbell_hfd <- FindVariableGenes(object = seurat_campbell_hfd,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.05,
x.high.cutoff = 3,
y.cutoff = 0.75,
num.bin = 20,
binning.method = "equal_width"
)
# number of variable genes
length(seurat_campbell_hfd@var.genes)## [1] 1454seurat_campbell_hfd <- FindVariableGenes(object = seurat_campbell_hfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1459 variable genesseurat_campbell_hfd <- FindVariableGenes(object = seurat_campbell_hfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 2305 variable genesseurat_campbell_hfd <- FindVariableGenes(object = seurat_campbell_hfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1941 variable genesseurat_campbell_hfd <- FindVariableGenes(object = seurat_campbell_hfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 3085 variable genesseurat_campbell_hfd <- FindVariableGenes(object = seurat_campbell_hfd, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 40, binning.method = “equal_width” )
== 3034 variable geneScale normalised gene expression per cell to remove unwanted sources of variation
Scale gene expression per cell by building linear models for nUMI, percent_mito and mouse replicate.
## Scale data nUMI, percent_mito and mouse replicate
seurat_campbell_hfd <- ScaleData(object = seurat_campbell_hfd, vars.to.regress = c("nUMI", "percent_mito"))## Regressing out: nUMI, percent_mito##
## Time Elapsed: 31.4707977771759 secs## Scaling data matrixPrincipal component anlysis of variable genes.
Principal component anlysis of variable genes for use in cell clustering.
## Perform principal component analysis on variable genes
seurat_campbell_hfd <- RunPCA(object = seurat_campbell_hfd,
pc.genes = seurat_campbell_hfd@var.genes,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 5)## [1] "PC1"
## [1] "ENSMUSG00000026385" "ENSMUSG00000031765" "ENSMUSG00000031762"
## [4] "ENSMUSG00000001025" "ENSMUSG00000026701"
## [1] ""
## [1] "ENSMUSG00000050711" "ENSMUSG00000043388" "ENSMUSG00000027350"
## [4] "ENSMUSG00000024261" "ENSMUSG00000035864"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000050711" "ENSMUSG00000055254" "ENSMUSG00000037852"
## [4] "ENSMUSG00000043388" "ENSMUSG00000031760"
## [1] ""
## [1] "ENSMUSG00000027199" "ENSMUSG00000027375" "ENSMUSG00000037625"
## [4] "ENSMUSG00000032854" "ENSMUSG00000076439"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000037625" "ENSMUSG00000032060" "ENSMUSG00000076439"
## [4] "ENSMUSG00000026830" "ENSMUSG00000032854"
## [1] ""
## [1] "ENSMUSG00000020077" "ENSMUSG00000029484" "ENSMUSG00000079018"
## [4] "ENSMUSG00000041378" "ENSMUSG00000075602"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000036905" "ENSMUSG00000036887" "ENSMUSG00000036896"
## [4] "ENSMUSG00000030579" "ENSMUSG00000058715"
## [1] ""
## [1] "ENSMUSG00000041378" "ENSMUSG00000079018" "ENSMUSG00000030237"
## [4] "ENSMUSG00000030235" "ENSMUSG00000020154"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000094800"
## [4] "ENSMUSG00000072674" "ENSMUSG00000027744"
## [1] ""
## [1] "ENSMUSG00000022132" "ENSMUSG00000029838" "ENSMUSG00000022528"
## [4] "ENSMUSG00000024518" "ENSMUSG00000058897"
## [1] ""
## [1] ""## visualise top genes associated with principal components
VizPCA(object = seurat_campbell_hfd, pcs.use = 1:9)
## Plot principal component 1 v's 2
PCAPlot(object = seurat_campbell_hfd, dim.1 = 1, dim.2 = 2)
## Plot principal component 2 v's 3
PCAPlot(object = seurat_campbell_hfd, dim.1 = 2, dim.2 = 3)
## Plot heat map for gene expression of principal component 1 genes
PCHeatmap(object = seurat_campbell_hfd, pc.use = 1, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 2 genes
PCHeatmap(object = seurat_campbell_hfd, pc.use = 2, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 3 genes
PCHeatmap(object = seurat_campbell_hfd, pc.use = 3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 4 genes
PCHeatmap(object = seurat_campbell_hfd, pc.use = 4, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot multiple heatmaps of gene expression per PC genes
PCHeatmap(object = seurat_campbell_hfd,
pc.use = 5:18,
cells.use = 500,
do.balanced = TRUE,
label.columns = FALSE)
## Takes a pre-computed PCA (from most variable genes identified earlier) and projects this onto the entire dataset (all genes)
seurat_campbell_hfd <- ProjectPCA(object = seurat_campbell_hfd, do.print = TRUE)## [1] "PC1"
## [1] "ENSMUSG00000002985" "ENSMUSG00000026385" "ENSMUSG00000031765"
## [4] "ENSMUSG00000031762" "ENSMUSG00000001025" "ENSMUSG00000026701"
## [7] "ENSMUSG00000035805" "ENSMUSG00000050953" "ENSMUSG00000026728"
## [10] "ENSMUSG00000000567" "ENSMUSG00000067786" "ENSMUSG00000083138"
## [13] "ENSMUSG00000018593" "ENSMUSG00000058135" "ENSMUSG00000095538"
## [16] "ENSMUSG00000027712" "ENSMUSG00000005360" "ENSMUSG00000034467"
## [19] "ENSMUSG00000022528" "ENSMUSG00000026649" "ENSMUSG00000018102"
## [22] "ENSMUSG00000053931" "ENSMUSG00000017009" "ENSMUSG00000029838"
## [25] "ENSMUSG00000044080" "ENSMUSG00000036570" "ENSMUSG00000032231"
## [28] "ENSMUSG00000030342" "ENSMUSG00000031342" "ENSMUSG00000055254"
## [1] ""
## [1] "ENSMUSG00000033061" "ENSMUSG00000027581" "ENSMUSG00000050711"
## [4] "ENSMUSG00000047261" "ENSMUSG00000025468" "ENSMUSG00000022577"
## [7] "ENSMUSG00000026576" "ENSMUSG00000024268" "ENSMUSG00000019986"
## [10] "ENSMUSG00000029223" "ENSMUSG00000021087" "ENSMUSG00000043388"
## [13] "ENSMUSG00000042750" "ENSMUSG00000040785" "ENSMUSG00000055430"
## [16] "ENSMUSG00000044349" "ENSMUSG00000027500" "ENSMUSG00000036699"
## [19] "ENSMUSG00000019923" "ENSMUSG00000027350" "ENSMUSG00000035964"
## [22] "ENSMUSG00000027273" "ENSMUSG00000031840" "ENSMUSG00000048978"
## [25] "ENSMUSG00000060188" "ENSMUSG00000000159" "ENSMUSG00000033981"
## [28] "ENSMUSG00000020297" "ENSMUSG00000022658" "ENSMUSG00000059361"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000018451" "ENSMUSG00000021268" "ENSMUSG00000026223"
## [4] "ENSMUSG00000031428" "ENSMUSG00000033585" "ENSMUSG00000039278"
## [7] "ENSMUSG00000019986" "ENSMUSG00000026576" "ENSMUSG00000040785"
## [10] "ENSMUSG00000050071" "ENSMUSG00000006373" "ENSMUSG00000042750"
## [13] "ENSMUSG00000046432" "ENSMUSG00000055430" "ENSMUSG00000025579"
## [16] "ENSMUSG00000002265" "ENSMUSG00000022577" "ENSMUSG00000029223"
## [19] "ENSMUSG00000033061" "ENSMUSG00000043384" "ENSMUSG00000072964"
## [22] "ENSMUSG00000034723" "ENSMUSG00000050711" "ENSMUSG00000055254"
## [25] "ENSMUSG00000049422" "ENSMUSG00000024268" "ENSMUSG00000025468"
## [28] "ENSMUSG00000019923" "ENSMUSG00000058254" "ENSMUSG00000037852"
## [1] ""
## [1] "ENSMUSG00000027199" "ENSMUSG00000027375" "ENSMUSG00000037625"
## [4] "ENSMUSG00000032854" "ENSMUSG00000031425" "ENSMUSG00000076439"
## [7] "ENSMUSG00000031775" "ENSMUSG00000036634" "ENSMUSG00000026830"
## [10] "ENSMUSG00000022548" "ENSMUSG00000032517" "ENSMUSG00000027562"
## [13] "ENSMUSG00000050121" "ENSMUSG00000090639" "ENSMUSG00000022425"
## [16] "ENSMUSG00000020486" "ENSMUSG00000032060" "ENSMUSG00000033579"
## [19] "ENSMUSG00000041607" "ENSMUSG00000073680" "ENSMUSG00000006782"
## [22] "ENSMUSG00000046160" "ENSMUSG00000015090" "ENSMUSG00000070354"
## [25] "ENSMUSG00000037166" "ENSMUSG00000026888" "ENSMUSG00000020774"
## [28] "ENSMUSG00000022090" "ENSMUSG00000013523" "ENSMUSG00000027858"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000037625" "ENSMUSG00000032060" "ENSMUSG00000076439"
## [4] "ENSMUSG00000026830" "ENSMUSG00000031425" "ENSMUSG00000032854"
## [7] "ENSMUSG00000050121" "ENSMUSG00000006782" "ENSMUSG00000040759"
## [10] "ENSMUSG00000036634" "ENSMUSG00000032517" "ENSMUSG00000046160"
## [13] "ENSMUSG00000033579" "ENSMUSG00000015090" "ENSMUSG00000025203"
## [16] "ENSMUSG00000116358" "ENSMUSG00000073680" "ENSMUSG00000027858"
## [19] "ENSMUSG00000031342" "ENSMUSG00000068923" "ENSMUSG00000020774"
## [22] "ENSMUSG00000013523" "ENSMUSG00000022425" "ENSMUSG00000041607"
## [25] "ENSMUSG00000037166" "ENSMUSG00000030701" "ENSMUSG00000043448"
## [28] "ENSMUSG00000027562" "ENSMUSG00000050854" "ENSMUSG00000090996"
## [1] ""
## [1] "ENSMUSG00000020077" "ENSMUSG00000029484" "ENSMUSG00000079018"
## [4] "ENSMUSG00000041378" "ENSMUSG00000075602" "ENSMUSG00000030237"
## [7] "ENSMUSG00000030235" "ENSMUSG00000040584" "ENSMUSG00000020154"
## [10] "ENSMUSG00000056492" "ENSMUSG00000001123" "ENSMUSG00000058715"
## [13] "ENSMUSG00000036887" "ENSMUSG00000036905" "ENSMUSG00000032359"
## [16] "ENSMUSG00000016494" "ENSMUSG00000030579" "ENSMUSG00000036896"
## [19] "ENSMUSG00000028581" "ENSMUSG00000114487" "ENSMUSG00000024621"
## [22] "ENSMUSG00000039167" "ENSMUSG00000001946" "ENSMUSG00000036256"
## [25] "ENSMUSG00000022584" "ENSMUSG00000023992" "ENSMUSG00000021423"
## [28] "ENSMUSG00000020717" "ENSMUSG00000024397" "ENSMUSG00000033960"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000036905" "ENSMUSG00000036887" "ENSMUSG00000036896"
## [4] "ENSMUSG00000030579" "ENSMUSG00000058715" "ENSMUSG00000028581"
## [7] "ENSMUSG00000024621" "ENSMUSG00000023992" "ENSMUSG00000021423"
## [10] "ENSMUSG00000024397" "ENSMUSG00000038642" "ENSMUSG00000015852"
## [13] "ENSMUSG00000036353" "ENSMUSG00000048163" "ENSMUSG00000046805"
## [16] "ENSMUSG00000059498" "ENSMUSG00000000682" "ENSMUSG00000052160"
## [19] "ENSMUSG00000054675" "ENSMUSG00000002111" "ENSMUSG00000030786"
## [22] "ENSMUSG00000040747" "ENSMUSG00000040229" "ENSMUSG00000036908"
## [25] "ENSMUSG00000044811" "ENSMUSG00000027848" "ENSMUSG00000069516"
## [28] "ENSMUSG00000018008" "ENSMUSG00000021665" "ENSMUSG00000026786"
## [1] ""
## [1] "ENSMUSG00000041378" "ENSMUSG00000079018" "ENSMUSG00000030237"
## [4] "ENSMUSG00000030235" "ENSMUSG00000020154" "ENSMUSG00000040584"
## [7] "ENSMUSG00000056492" "ENSMUSG00000075602" "ENSMUSG00000039167"
## [10] "ENSMUSG00000001946" "ENSMUSG00000033960" "ENSMUSG00000022584"
## [13] "ENSMUSG00000036256" "ENSMUSG00000029648" "ENSMUSG00000114487"
## [16] "ENSMUSG00000020717" "ENSMUSG00000000805" "ENSMUSG00000039349"
## [19] "ENSMUSG00000054690" "ENSMUSG00000042116" "ENSMUSG00000031239"
## [22] "ENSMUSG00000027435" "ENSMUSG00000006386" "ENSMUSG00000026814"
## [25] "ENSMUSG00000061353" "ENSMUSG00000062960" "ENSMUSG00000024168"
## [28] "ENSMUSG00000024140" "ENSMUSG00000026193" "ENSMUSG00000051669"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000070306" "ENSMUSG00000043164" "ENSMUSG00000094800"
## [4] "ENSMUSG00000072674" "ENSMUSG00000027744" "ENSMUSG00000095304"
## [7] "ENSMUSG00000045655" "ENSMUSG00000108841" "ENSMUSG00000047671"
## [10] "ENSMUSG00000044772" "ENSMUSG00000020473" "ENSMUSG00000032595"
## [13] "ENSMUSG00000110332" "ENSMUSG00000027800" "ENSMUSG00000027867"
## [16] "ENSMUSG00000029182" "ENSMUSG00000038370" "ENSMUSG00000072473"
## [19] "ENSMUSG00000085416" "ENSMUSG00000051606" "ENSMUSG00000047139"
## [22] "ENSMUSG00000047394" "ENSMUSG00000037716" "ENSMUSG00000027360"
## [25] "ENSMUSG00000056174" "ENSMUSG00000046242" "ENSMUSG00000044475"
## [28] "ENSMUSG00000041323" "ENSMUSG00000060981" "ENSMUSG00000086742"
## [1] ""
## [1] "ENSMUSG00000022132" "ENSMUSG00000029838" "ENSMUSG00000022528"
## [4] "ENSMUSG00000024518" "ENSMUSG00000058897" "ENSMUSG00000021250"
## [7] "ENSMUSG00000063564" "ENSMUSG00000007682" "ENSMUSG00000018451"
## [10] "ENSMUSG00000045005" "ENSMUSG00000024190" "ENSMUSG00000035686"
## [13] "ENSMUSG00000033737" "ENSMUSG00000038418" "ENSMUSG00000034810"
## [16] "ENSMUSG00000017390" "ENSMUSG00000047786" "ENSMUSG00000061718"
## [19] "ENSMUSG00000028195" "ENSMUSG00000030629" "ENSMUSG00000003545"
## [22] "ENSMUSG00000027004" "ENSMUSG00000055653" "ENSMUSG00000052684"
## [25] "ENSMUSG00000034640" "ENSMUSG00000022419" "ENSMUSG00000041891"
## [28] "ENSMUSG00000053560" "ENSMUSG00000050708" "ENSMUSG00000030428"
## [1] ""
## [1] ""Determine statistically significant principal components
## Perform jackstraw statistical test to investigate statistically significant PC.
seurat_campbell_hfd <- JackStraw(object = seurat_campbell_hfd,
num.replicate = 100,
display.progress = TRUE
)##
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## Time Elapsed: 2.13899685939153 mins# Maximum number of PCs allowed = 20.
## Visualise JackStraw plots
JackStrawPlot(object = seurat_campbell_hfd, PCs = 1:20)## Warning: Removed 20637 rows containing missing values (geom_point).
## An object of class seurat in project CAMPBELL_HFD
## 23685 genes across 3482 samples.## A less computationally intensive heuristic method for finding the statistically significant PCAs is using an elbow plot
PCElbowPlot(object = seurat_campbell_hfd)
Cell clustering
## Cluster cells by PC
seurat_campbell_hfd <- FindClusters(object = seurat_campbell_hfd, reduction.type = "pca", dims.use = 1:14, resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_hfd)## Parameters used in latest FindClusters calculation run on: 2018-11-20 23:57:51
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14seurat_campbell_hfd <- RunTSNE(object = seurat_campbell_hfd, dims.use = 1:14, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_hfd)
TSNEPlot(object = seurat_campbell_hfd,
no.legend = TRUE,
do.label = TRUE)
## 1454 variable genes, 20 PC = 14 clusters
## 1454 variable genes, 14 PC = 14 clusters
table(seurat_campbell_hfd@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13
## 744 629 620 308 225 168 148 139 137 133 76 66 51 38#######
seurat_campbell_hfd <- FindClusters(object = seurat_campbell_hfd, reduction.type = "pca", dims.use = 1:14,
resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_hfd)## Parameters used in latest FindClusters calculation run on: 2018-11-20 23:57:51
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14seurat_campbell_hfd <- RunTSNE(object = seurat_campbell_hfd, dims.use = 1:14, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_hfd)
TSNEPlot(object = seurat_campbell_hfd,
no.legend = TRUE,
do.label = TRUE)
## 1454 variable genes, 20 PC = 16 clusters
## 1454 variable genes, 14 PC = 16 clusters
table(seurat_campbell_hfd@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 738 623 400 226 223 177 156 154 148 139 137 133 76 63 51 38#######
seurat_campbell_hfd <- FindClusters(object = seurat_campbell_hfd, reduction.type = "pca", dims.use = 1:14,
resolution = 1.5, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_hfd)## Parameters used in latest FindClusters calculation run on: 2018-11-20 23:57:51
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14seurat_campbell_hfd <- RunTSNE(object = seurat_campbell_hfd, dims.use = 1:14, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_hfd)
TSNEPlot(object = seurat_campbell_hfd,
no.legend = TRUE,
do.label = TRUE)
## 1454 variable genes, 20 PC = 20 clusters
## 1454 variable genes, 14 PC = 18 clusters
table(seurat_campbell_hfd@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 549 400 363 256 227 223 198 167 157 154 148 140 137 133 76 63 51 40#######
seurat_campbell_hfd <- FindClusters(object = seurat_campbell_hfd, reduction.type = "pca", dims.use = 1:14,
resolution = 2.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_hfd)## Parameters used in latest FindClusters calculation run on: 2018-11-20 23:57:51
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14seurat_campbell_hfd <- RunTSNE(object = seurat_campbell_hfd, dims.use = 1:14, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_hfd)
TSNEPlot(object = seurat_campbell_hfd,
no.legend = TRUE,
do.label = TRUE)
## 1454 variable genes, 20 PC = 21 clusters
## 1454 variable genes, 14 PC = 21 clusters
table(seurat_campbell_hfd@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 344 331 244 239 232 227 202 199 191 174 157 152 139 137 133 118 76 66
## 18 19 20
## 51 40 30## Cluster cells using final parameters (1454 genes, 14 PC, resolution = 1.0)
seurat_campbell_hfd <- FindClusters(object = seurat_campbell_hfd, reduction.type = "pca", dims.use = 1:14, resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
## Produce t-SNE of final parameters:
seurat_campbell_hfd <- RunTSNE(object = seurat_campbell_hfd, dims.use = 1:14, do.fast = TRUE)Use 1454 variable genes and 14 principal components with a resolution of 1.0 (Changing number of PC makes almost no difference to the clustering). This gives a total of 16 clusters.
Finding differentially expressed genes between cell clusters (cluster biomarkers)
## Find markers for every cluster compared to all remaining cells, report both positive and negative genes.
seurat_campbell_hfd_biomarkers <- FindAllMarkers(object = seurat_campbell_hfd, only.pos = FALSE, min.pct = 0.2)
## Get the top 10 biomarkers per cluster
top10_seurat_campbell_markers<- seurat_campbell_hfd_biomarkers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers## # A tibble: 160 x 7
## # Groups: cluster [16]
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 4.15e-142 0.909 0.943 0.503 9.82e-138 0 ENSMUSG00000000159
## 2 7.49e-118 0.658 0.973 0.582 1.77e-113 0 ENSMUSG00000047261
## 3 1.07e-106 0.603 0.978 0.774 2.53e-102 0 ENSMUSG00000055430
## 4 9.01e-101 0.605 0.932 0.499 2.14e- 96 0 ENSMUSG00000027500
## 5 8.43e- 93 0.570 0.881 0.427 2.00e- 88 0 ENSMUSG00000048978
## 6 6.28e- 91 0.596 0.64 0.227 1.49e- 86 0 ENSMUSG00000021032
## 7 3.92e- 23 0.896 0.215 0.087 9.27e- 19 0 ENSMUSG00000032291
## 8 1.10e- 22 0.701 0.375 0.203 2.61e- 18 0 ENSMUSG00000020660
## 9 9.08e- 22 0.683 0.404 0.229 2.15e- 17 0 ENSMUSG00000021647
## 10 6.81e- 17 1.09 0.289 0.158 1.61e- 12 0 ENSMUSG00000004366
## # ... with 150 more rowswrite.csv(as.data.frame(seurat_campbell_hfd_biomarkers), file = "seurat_campbell_hfd_biomarkers.csv", quote = FALSE)
write.csv(as.data.frame(top10_seurat_campbell_markers), file = "top10_seurat_campbell_hfd_biomarkers.csv", quote = FALSE)
## Perform ROC DE test. This can take a long time.
seurat_campbell_hfd_biomarkers_ROC <- FindAllMarkers(object = seurat_campbell_hfd, only.pos = FALSE, min.pct = 0.2, test.use = "roc")
top10_seurat_campbell_markers_ROC<- seurat_campbell_hfd_biomarkers_ROC %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers_ROC## # A tibble: 160 x 9
## # Groups: cluster [16]
## myAUC avg_diff power avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0.794 0.909 0.588 0.909 0.943 0.503 NA 0 ENSMUSG00…
## 2 0.771 0.658 0.542 0.658 0.973 0.582 NA 0 ENSMUSG00…
## 3 0.762 0.603 0.524 0.603 0.978 0.774 NA 0 ENSMUSG00…
## 4 0.746 0.605 0.492 0.605 0.932 0.499 NA 0 ENSMUSG00…
## 5 0.737 0.548 0.474 0.548 0.991 0.771 NA 0 ENSMUSG00…
## 6 0.736 0.558 0.472 0.558 0.921 0.474 NA 0 ENSMUSG00…
## 7 0.731 0.570 0.462 0.570 0.881 0.427 NA 0 ENSMUSG00…
## 8 0.729 0.551 0.458 0.551 0.935 0.561 NA 0 ENSMUSG00…
## 9 0.727 0.533 0.454 0.533 0.972 0.547 NA 0 ENSMUSG00…
## 10 0.722 0.555 0.444 0.555 0.989 0.88 NA 0 ENSMUSG00…
## # ... with 150 more rowswrite.csv(as.data.frame(seurat_campbell_hfd_biomarkers_ROC), file = "seurat_campbell_hfd_biomarkers_ROC.csv", quote = FALSE)
write.csv(as.data.frame(top10_seurat_campbell_markers_ROC), file = "top10_seurat_campbell_hfd_biomarkers_ROC.csv", quote = FALSE)
## Plot heatmap of top 10 DE genes
DoHeatmap(object = seurat_campbell_hfd,
genes.use = top10_seurat_campbell_markers$gene,
slim.col.label = TRUE,
remove.key = TRUE)
Save seurat object of chow mice.
## save seurat object as .rds
saveRDS(seurat_campbell_hfd, file = "./seurat_campbell_hfd_final.rds")Analysis of Campbell scRNAseq data for mice fasted for 24h and then REFED for 2h (1 replicate) using seurat
Load REFED mouse data
## Load seurat object
seurat_campbell_refed<- readRDS(file = "./seurat_campbell_refed_just_created.rds")
seurat_campbell_refed## An object of class seurat in project CAMPBELL_REFED
## 19267 genes across 2375 samples.Quality control on CHOW mouse data
## Import list of mouse mitocondrially encoded genes:
mito_genes<- read.csv("mito_genes_table.csv", header = TRUE)
## Make list of mitocondrial gene IDS:
mito_genes <- mito_genes$id_with_url
## Identify which mitochondrially expressed gene IDs are present in my dataset:
mito_genes_present<-seurat_campbell_refed@raw.data[mito_genes, ]
mito_genes_present<- mito_genes[!grepl("NA", rownames(mito_genes_present))]
seurat_campbell_refed@raw.data[mito_genes_present, 1:5]## AAAAATACCGGC AAAAATAGGCGT AAAAATAGGGTT AAAACATTACTT
## ENSMUSG00000000028 0 0 0 0
## ENSMUSG00000000037 0 0 0 0
## ENSMUSG00000000154 0 0 0 0
## ENSMUSG00000000149 0 0 0 0
## ENSMUSG00000000214 0 0 0 1
## ENSMUSG00000000159 0 0 2 2
## ENSMUSG00000000142 0 0 0 0
## ENSMUSG00000000127 0 1 1 0
## ENSMUSG00000000085 1 0 0 0
## ENSMUSG00000000197 0 0 1 1
## ENSMUSG00000000093 0 0 0 0
## ENSMUSG00000000168 0 0 0 0
## ENSMUSG00000000120 0 0 0 0
## ENSMUSG00000000134 0 0 0 0
## ENSMUSG00000000125 0 0 0 0
## ENSMUSG00000000202 0 0 0 0
## ENSMUSG00000000058 0 0 0 0
## ENSMUSG00000000167 0 0 0 0
## ENSMUSG00000000171 1 0 0 0
## ENSMUSG00000000056 0 1 0 0
## ENSMUSG00000000049 0 0 0 0
## ENSMUSG00000000194 0 0 0 0
## ENSMUSG00000000148 0 0 0 0
## ENSMUSG00000000078 1 0 0 0
## ENSMUSG00000000001 0 0 0 0
## ENSMUSG00000000247 0 0 0 0
## ENSMUSG00000000131 0 0 0 0
## ENSMUSG00000000184 0 1 0 0
## ENSMUSG00000000223 0 0 2 0
## ENSMUSG00000000088 0 0 0 0
## ENSMUSG00000000126 0 0 0 0
## ENSMUSG00000000031 0 0 0 0
## AAAAGACTAGTA
## ENSMUSG00000000028 0
## ENSMUSG00000000037 0
## ENSMUSG00000000154 0
## ENSMUSG00000000149 0
## ENSMUSG00000000214 0
## ENSMUSG00000000159 1
## ENSMUSG00000000142 0
## ENSMUSG00000000127 0
## ENSMUSG00000000085 0
## ENSMUSG00000000197 0
## ENSMUSG00000000093 0
## ENSMUSG00000000168 0
## ENSMUSG00000000120 0
## ENSMUSG00000000134 0
## ENSMUSG00000000125 0
## ENSMUSG00000000202 0
## ENSMUSG00000000058 0
## ENSMUSG00000000167 0
## ENSMUSG00000000171 0
## ENSMUSG00000000056 0
## ENSMUSG00000000049 0
## ENSMUSG00000000194 0
## ENSMUSG00000000148 0
## ENSMUSG00000000078 0
## ENSMUSG00000000001 0
## ENSMUSG00000000247 0
## ENSMUSG00000000131 0
## ENSMUSG00000000184 0
## ENSMUSG00000000223 0
## ENSMUSG00000000088 0
## ENSMUSG00000000126 0
## ENSMUSG00000000031 0dim(seurat_campbell_refed@raw.data[mito_genes_present, ])## [1] 32 2375## Calculate the percentage of mitcondrial gene counts per cell
percent_mito <- Matrix::colSums(seurat_campbell_refed@raw.data[mito_genes_present, ])/Matrix::colSums(seurat_campbell_refed@raw.data)
## Basic stats of proportion of mitocondrial gene expression per cell
summary(percent_mito)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0008132 0.0015576 0.0018138 0.0025196 0.0097259## add percentage mitocondrial genes into metadata
seurat_campbell_refed <- AddMetaData(object = seurat_campbell_refed,
metadata = percent_mito,
col.name = "percent_mito")
## Look at the seurat object meta data
head(seurat_campbell_refed@meta.data)## nGene nUMI orig.ident replicate_name percent_mito
## AAAAATACCGGC 1271 2224 CAMPBELL_REFED SRR5164442 0.0013519603
## AAAAATAGGCGT 1088 1900 CAMPBELL_REFED SRR5164442 0.0015797788
## AAAAATAGGGTT 3056 6629 CAMPBELL_REFED SRR5164442 0.0009057971
## AAAACATTACTT 841 1535 CAMPBELL_REFED SRR5164442 0.0026058632
## AAAAGACTAGTA 1195 1925 CAMPBELL_REFED SRR5164442 0.0005208333
## AAAAGATCATAC 966 1827 CAMPBELL_REFED SRR5164442 0.0005473454## QC plots of number of genes, UMIs, and % mitochondria
VlnPlot(object = seurat_campbell_refed,
features.plot = c("nGene", "nUMI", "percent_mito"),
nCol = 3,
x.lab.rot = TRUE,
point.size.use = 0.2
)
## QC plots to show the relationship between nUMIs and relative mitocondrial gene expression or number of genes.
par(mfrow = c(1, 2))
# GenePlot(object = seurat_campbell_refed, gene1 = "nUMI", gene2 = "percent_mito", do.hover = TRUE, pch.use = 1)
# GenePlot(object = seurat_campbell_refed, gene1 = "nUMI", gene2 = "nGene", do.hover = TRUE, pch.use = 1)
GenePlot(object = seurat_campbell_refed, gene1 = "nUMI", gene2 = "percent_mito", pch.use = 16, cex.use = 0.5)
GenePlot(object = seurat_campbell_refed, gene1 = "nUMI", gene2 = "nGene", pch.use = 16, cex.use = 0.5)
Filter cells after QC
Filter cells out with more than 0.4% of total gene expression comming from mitocondrially encoded genes and more than 3500 genes expressed.
## manual check; I already know all cells have >800 genes
table(seurat_campbell_refed@meta.data$percent_mito < 0.004 & seurat_campbell_refed@meta.data$nGene<3500)##
## FALSE TRUE
## 201 2174# FALSE TRUE
# 201 2174
## Filter cells with <0.4% percent_mito and <3500 genes
seurat_campbell_refed <- FilterCells(object = seurat_campbell_refed,
subset.names = c("nGene", "percent_mito"),
low.thresholds = c(800, -Inf),
high.thresholds = c(3500, 0.004))
seurat_campbell_refed## An object of class seurat in project CAMPBELL_REFED
## 19267 genes across 2174 samples.# An object of class seurat in project CAMPBELL_REFED
# 19267 genes across 2174 samples.Log normalise gene expression per cell
## Plot graph of total expression before normalisation
hist(colSums(seurat_campbell_refed@data),
breaks = 100,
main = "Total expression before normalisation",
xlab = "Sum of expression")
## Normalise gene expression per cell
seurat_campbell_refed <- NormalizeData(object = seurat_campbell_refed, normalization.method = "LogNormalize", scale.factor = 10000)
## Plot graph of total expression after normalisation
hist(colSums(as.data.frame(as.matrix(seurat_campbell_refed@data))),
breaks = 100,
main = "Total expression after normalisation",
xlab = "Sum of expression")
Find genes whose expression varies between cells.
Find genes whose expression varies between cells, which will be used to construct principal componets between cells that will be used for clustering.
## Find variable genes by expression
seurat_campbell_refed <- FindVariableGenes(object = seurat_campbell_refed,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.05,
x.high.cutoff = 3,
y.cutoff = 0.75,
num.bin = 20,
binning.method = "equal_width"
)
# number of variable genes
length(seurat_campbell_refed@var.genes)## [1] 1451seurat_campbell_refed <- FindVariableGenes(object = seurat_campbell_refed, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 4, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1456 variable genesseurat_campbell_refed <- FindVariableGenes(object = seurat_campbell_refed, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.05, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 2293 variable genesseurat_campbell_refed <- FindVariableGenes(object = seurat_campbell_refed, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.75, num.bin = 20, binning.method = “equal_width” )
== 1916 variable genesseurat_campbell_refed <- FindVariableGenes(object = seurat_campbell_refed, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 20, binning.method = “equal_width” )
== 2916 variable genesseurat_campbell_refed <- FindVariableGenes(object = seurat_campbell_refed, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.02, x.high.cutoff = 3, y.cutoff = 0.5, num.bin = 40, binning.method = “equal_width” )
== 2831 variable geneScale normalised gene expression per cell to remove unwanted sources of variation
Scale gene expression per cell by building linear models for nUMI, percent_mito and mouse replicate.
## Scale data nUMI, percent_mito and mouse replicate
seurat_campbell_refed <- ScaleData(object = seurat_campbell_refed, vars.to.regress = c("nUMI", "percent_mito"))## Regressing out: nUMI, percent_mito##
## Time Elapsed: 22.1721687316895 secs## Scaling data matrixPrincipal component anlysis of variable genes.
Principal component anlysis of variable genes for use in cell clustering.
## Perform principal component analysis on variable genes
seurat_campbell_refed <- RunPCA(object = seurat_campbell_refed,
pc.genes = seurat_campbell_refed@var.genes,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 5)## [1] "PC1"
## [1] "ENSMUSG00000027500" "ENSMUSG00000027350" "ENSMUSG00000035864"
## [4] "ENSMUSG00000040856" "ENSMUSG00000026787"
## [1] ""
## [1] "ENSMUSG00000026385" "ENSMUSG00000031765" "ENSMUSG00000050953"
## [4] "ENSMUSG00000018593" "ENSMUSG00000000567"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000079018" "ENSMUSG00000041378" "ENSMUSG00000040584"
## [4] "ENSMUSG00000020154" "ENSMUSG00000056492"
## [1] ""
## [1] "ENSMUSG00000055254" "ENSMUSG00000067786" "ENSMUSG00000000567"
## [4] "ENSMUSG00000035805" "ENSMUSG00000026701"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000027350" "ENSMUSG00000027800" "ENSMUSG00000075602"
## [4] "ENSMUSG00000023175" "ENSMUSG00000038370"
## [1] ""
## [1] "ENSMUSG00000037625" "ENSMUSG00000036634" "ENSMUSG00000031425"
## [4] "ENSMUSG00000032517" "ENSMUSG00000032854"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000020423" "ENSMUSG00000003545" "ENSMUSG00000022528"
## [4] "ENSMUSG00000022132" "ENSMUSG00000026185"
## [1] ""
## [1] "ENSMUSG00000043164" "ENSMUSG00000070306" "ENSMUSG00000094800"
## [4] "ENSMUSG00000110332" "ENSMUSG00000020473"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000029838" "ENSMUSG00000047786" "ENSMUSG00000055254"
## [4] "ENSMUSG00000063564" "ENSMUSG00000007682"
## [1] ""
## [1] "ENSMUSG00000056973" "ENSMUSG00000028298" "ENSMUSG00000032532"
## [4] "ENSMUSG00000032081" "ENSMUSG00000045394"
## [1] ""
## [1] ""## visualise top genes associated with principal components
VizPCA(object = seurat_campbell_refed, pcs.use = 1:9)
## Plot principal component 1 v's 2
PCAPlot(object = seurat_campbell_refed, dim.1 = 1, dim.2 = 2)
## Plot principal component 2 v's 3
PCAPlot(object = seurat_campbell_refed, dim.1 = 2, dim.2 = 3)
## Plot heat map for gene expression of principal component 1 genes
PCHeatmap(object = seurat_campbell_refed, pc.use = 1, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 2 genes
PCHeatmap(object = seurat_campbell_refed, pc.use = 2, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 3 genes
PCHeatmap(object = seurat_campbell_refed, pc.use = 3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot heat map for gene expression of principal component 4 genes
PCHeatmap(object = seurat_campbell_refed, pc.use = 4, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col =
## col.use, : Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting
## row dendogram.## Warning in heatmap.2(data.use, Rowv = NA, Colv = NA, trace = "none", col
## = col.use, : Discrepancy: Colv is FALSE, while dendrogram is `column'.
## Omitting column dendogram.## Warning in plot.window(...): "dimTitle" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "dimTitle" is not a graphical parameter## Warning in title(...): "dimTitle" is not a graphical parameter
# Ignore warnings!
## Plot multiple heatmaps of gene expression per PC genes
PCHeatmap(object = seurat_campbell_refed,
pc.use = 5:18,
cells.use = 500,
do.balanced = TRUE,
label.columns = FALSE)
## Takes a pre-computed PCA (from most variable genes identified earlier) and projects this onto the entire dataset (all genes)
seurat_campbell_refed <- ProjectPCA(object = seurat_campbell_refed, do.print = TRUE)## [1] "PC1"
## [1] "ENSMUSG00000050711" "ENSMUSG00000033061" "ENSMUSG00000044349"
## [4] "ENSMUSG00000021268" "ENSMUSG00000019986" "ENSMUSG00000055430"
## [7] "ENSMUSG00000027581" "ENSMUSG00000021087" "ENSMUSG00000029223"
## [10] "ENSMUSG00000040785" "ENSMUSG00000026576" "ENSMUSG00000043388"
## [13] "ENSMUSG00000024268" "ENSMUSG00000047261" "ENSMUSG00000024261"
## [16] "ENSMUSG00000027273" "ENSMUSG00000019923" "ENSMUSG00000042750"
## [19] "ENSMUSG00000025468" "ENSMUSG00000000159" "ENSMUSG00000036699"
## [22] "ENSMUSG00000002265" "ENSMUSG00000022577" "ENSMUSG00000107169"
## [25] "ENSMUSG00000024423" "ENSMUSG00000025579" "ENSMUSG00000027500"
## [28] "ENSMUSG00000003363" "ENSMUSG00000027350" "ENSMUSG00000033981"
## [1] ""
## [1] "ENSMUSG00000002985" "ENSMUSG00000026385" "ENSMUSG00000031765"
## [4] "ENSMUSG00000050953" "ENSMUSG00000018593" "ENSMUSG00000000567"
## [7] "ENSMUSG00000026728" "ENSMUSG00000001025" "ENSMUSG00000058135"
## [10] "ENSMUSG00000031762" "ENSMUSG00000026701" "ENSMUSG00000035805"
## [13] "ENSMUSG00000030342" "ENSMUSG00000005360" "ENSMUSG00000008575"
## [16] "ENSMUSG00000067786" "ENSMUSG00000018102" "ENSMUSG00000034467"
## [19] "ENSMUSG00000095538" "ENSMUSG00000028565" "ENSMUSG00000017390"
## [22] "ENSMUSG00000053931" "ENSMUSG00000032231" "ENSMUSG00000026547"
## [25] "ENSMUSG00000028517" "ENSMUSG00000036570" "ENSMUSG00000028195"
## [28] "ENSMUSG00000021250" "ENSMUSG00000044080" "ENSMUSG00000062078"
## [1] ""
## [1] ""
## [1] "PC2"
## [1] "ENSMUSG00000079018" "ENSMUSG00000041378" "ENSMUSG00000040584"
## [4] "ENSMUSG00000020154" "ENSMUSG00000056492" "ENSMUSG00000033960"
## [7] "ENSMUSG00000075602" "ENSMUSG00000030237" "ENSMUSG00000001946"
## [10] "ENSMUSG00000042745" "ENSMUSG00000030235" "ENSMUSG00000039167"
## [13] "ENSMUSG00000029648" "ENSMUSG00000020717" "ENSMUSG00000054404"
## [16] "ENSMUSG00000061353" "ENSMUSG00000034738" "ENSMUSG00000006386"
## [19] "ENSMUSG00000022584" "ENSMUSG00000031871" "ENSMUSG00000029802"
## [22] "ENSMUSG00000040732" "ENSMUSG00000114487" "ENSMUSG00000027435"
## [25] "ENSMUSG00000020077" "ENSMUSG00000062960" "ENSMUSG00000032035"
## [28] "ENSMUSG00000054690" "ENSMUSG00000031239" "ENSMUSG00000042116"
## [1] ""
## [1] "ENSMUSG00000018451" "ENSMUSG00000055254" "ENSMUSG00000067786"
## [4] "ENSMUSG00000000567" "ENSMUSG00000035805" "ENSMUSG00000026701"
## [7] "ENSMUSG00000001025" "ENSMUSG00000026385" "ENSMUSG00000031760"
## [10] "ENSMUSG00000017390" "ENSMUSG00000031762" "ENSMUSG00000005360"
## [13] "ENSMUSG00000025666" "ENSMUSG00000034467" "ENSMUSG00000021379"
## [16] "ENSMUSG00000058135" "ENSMUSG00000031765" "ENSMUSG00000050953"
## [19] "ENSMUSG00000095538" "ENSMUSG00000004558" "ENSMUSG00000052727"
## [22] "ENSMUSG00000026223" "ENSMUSG00000036570" "ENSMUSG00000030428"
## [25] "ENSMUSG00000031342" "ENSMUSG00000024411" "ENSMUSG00000026649"
## [28] "ENSMUSG00000037852" "ENSMUSG00000026546" "ENSMUSG00000005089"
## [1] ""
## [1] ""
## [1] "PC3"
## [1] "ENSMUSG00000021268" "ENSMUSG00000050711" "ENSMUSG00000040785"
## [4] "ENSMUSG00000043384" "ENSMUSG00000033061" "ENSMUSG00000018451"
## [7] "ENSMUSG00000019986" "ENSMUSG00000026223" "ENSMUSG00000026576"
## [10] "ENSMUSG00000044349" "ENSMUSG00000027350" "ENSMUSG00000072964"
## [13] "ENSMUSG00000006373" "ENSMUSG00000036438" "ENSMUSG00000027800"
## [16] "ENSMUSG00000024261" "ENSMUSG00000075602" "ENSMUSG00000031428"
## [19] "ENSMUSG00000002265" "ENSMUSG00000022661" "ENSMUSG00000023175"
## [22] "ENSMUSG00000038370" "ENSMUSG00000042745" "ENSMUSG00000070802"
## [25] "ENSMUSG00000042750" "ENSMUSG00000056492" "ENSMUSG00000032181"
## [28] "ENSMUSG00000027712" "ENSMUSG00000024268" "ENSMUSG00000041378"
## [1] ""
## [1] "ENSMUSG00000037625" "ENSMUSG00000036634" "ENSMUSG00000031425"
## [4] "ENSMUSG00000032517" "ENSMUSG00000032854" "ENSMUSG00000076439"
## [7] "ENSMUSG00000041607" "ENSMUSG00000006782" "ENSMUSG00000026830"
## [10] "ENSMUSG00000027199" "ENSMUSG00000031775" "ENSMUSG00000115529"
## [13] "ENSMUSG00000032060" "ENSMUSG00000046160" "ENSMUSG00000020486"
## [16] "ENSMUSG00000056966" "ENSMUSG00000013523" "ENSMUSG00000038173"
## [19] "ENSMUSG00000022425" "ENSMUSG00000027562" "ENSMUSG00000015090"
## [22] "ENSMUSG00000090639" "ENSMUSG00000050121" "ENSMUSG00000032556"
## [25] "ENSMUSG00000027858" "ENSMUSG00000015149" "ENSMUSG00000070354"
## [28] "ENSMUSG00000027375" "ENSMUSG00000047976" "ENSMUSG00000040759"
## [1] ""
## [1] ""
## [1] "PC4"
## [1] "ENSMUSG00000020423" "ENSMUSG00000003545" "ENSMUSG00000022528"
## [4] "ENSMUSG00000022132" "ENSMUSG00000026185" "ENSMUSG00000007682"
## [7] "ENSMUSG00000052837" "ENSMUSG00000028195" "ENSMUSG00000038418"
## [10] "ENSMUSG00000021250" "ENSMUSG00000033737" "ENSMUSG00000030629"
## [13] "ENSMUSG00000028298" "ENSMUSG00000058897" "ENSMUSG00000052387"
## [16] "ENSMUSG00000029838" "ENSMUSG00000047786" "ENSMUSG00000019997"
## [19] "ENSMUSG00000032532" "ENSMUSG00000045005" "ENSMUSG00000044786"
## [22] "ENSMUSG00000032081" "ENSMUSG00000063564" "ENSMUSG00000034640"
## [25] "ENSMUSG00000003949" "ENSMUSG00000019960" "ENSMUSG00000034810"
## [28] "ENSMUSG00000045394" "ENSMUSG00000064351" "ENSMUSG00000022419"
## [1] ""
## [1] "ENSMUSG00000043164" "ENSMUSG00000070306" "ENSMUSG00000094800"
## [4] "ENSMUSG00000110332" "ENSMUSG00000020473" "ENSMUSG00000038370"
## [7] "ENSMUSG00000072674" "ENSMUSG00000032595" "ENSMUSG00000041323"
## [10] "ENSMUSG00000027800" "ENSMUSG00000045655" "ENSMUSG00000044772"
## [13] "ENSMUSG00000033208" "ENSMUSG00000047394" "ENSMUSG00000108841"
## [16] "ENSMUSG00000026683" "ENSMUSG00000047139" "ENSMUSG00000022037"
## [19] "ENSMUSG00000021879" "ENSMUSG00000027744" "ENSMUSG00000029182"
## [22] "ENSMUSG00000051606" "ENSMUSG00000021270" "ENSMUSG00000021950"
## [25] "ENSMUSG00000047361" "ENSMUSG00000044475" "ENSMUSG00000029309"
## [28] "ENSMUSG00000026301" "ENSMUSG00000056174" "ENSMUSG00000021087"
## [1] ""
## [1] ""
## [1] "PC5"
## [1] "ENSMUSG00000018451" "ENSMUSG00000029838" "ENSMUSG00000047786"
## [4] "ENSMUSG00000055254" "ENSMUSG00000063564" "ENSMUSG00000007682"
## [7] "ENSMUSG00000045005" "ENSMUSG00000017390" "ENSMUSG00000030428"
## [10] "ENSMUSG00000056380" "ENSMUSG00000015222" "ENSMUSG00000058897"
## [13] "ENSMUSG00000035686" "ENSMUSG00000027004" "ENSMUSG00000064339"
## [16] "ENSMUSG00000031342" "ENSMUSG00000033737" "ENSMUSG00000038156"
## [19] "ENSMUSG00000005360" "ENSMUSG00000052727" "ENSMUSG00000031626"
## [22] "ENSMUSG00000030905" "ENSMUSG00000058254" "ENSMUSG00000021087"
## [25] "ENSMUSG00000021732" "ENSMUSG00000055430" "ENSMUSG00000026701"
## [28] "ENSMUSG00000005089" "ENSMUSG00000040856" "ENSMUSG00000093460"
## [1] ""
## [1] "ENSMUSG00000056973" "ENSMUSG00000028298" "ENSMUSG00000032532"
## [4] "ENSMUSG00000032081" "ENSMUSG00000045394" "ENSMUSG00000015053"
## [7] "ENSMUSG00000027857" "ENSMUSG00000023043" "ENSMUSG00000066720"
## [10] "ENSMUSG00000039994" "ENSMUSG00000021194" "ENSMUSG00000028023"
## [13] "ENSMUSG00000051367" "ENSMUSG00000049382" "ENSMUSG00000021508"
## [16] "ENSMUSG00000021506" "ENSMUSG00000018569" "ENSMUSG00000052974"
## [19] "ENSMUSG00000031380" "ENSMUSG00000054889" "ENSMUSG00000027350"
## [22] "ENSMUSG00000026185" "ENSMUSG00000000037" "ENSMUSG00000036192"
## [25] "ENSMUSG00000040569" "ENSMUSG00000037664" "ENSMUSG00000039943"
## [28] "ENSMUSG00000068154" "ENSMUSG00000046743" "ENSMUSG00000017723"
## [1] ""
## [1] ""Determine statistically significant principal components
## Perform jackstraw statistical test to investigate statistically significant PC.
seurat_campbell_refed <- JackStraw(object = seurat_campbell_refed,
num.replicate = 100,
display.progress = TRUE
)##
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## Time Elapsed: 1.65351190964381 mins# Maximum number of PCs allowed = 20.
## Visualise JackStraw plots
JackStrawPlot(object = seurat_campbell_refed, PCs = 1:20)## Warning: Removed 20801 rows containing missing values (geom_point).
## An object of class seurat in project CAMPBELL_REFED
## 19267 genes across 2174 samples.## A less computationally intensive heuristic method for finding the statistically significant PCAs is using an elbow plot
PCElbowPlot(object = seurat_campbell_refed)
Cell clustering
## Cluster cells by PC
seurat_campbell_refed <- FindClusters(object = seurat_campbell_refed, reduction.type = "pca", dims.use = 1:13, resolution = 0.6, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_refed)## Parameters used in latest FindClusters calculation run on: 2018-11-21 00:44:18
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13seurat_campbell_refed <- RunTSNE(object = seurat_campbell_refed, dims.use = 1:13, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_refed)
TSNEPlot(object = seurat_campbell_refed,
no.legend = TRUE,
do.label = TRUE)
## 1451 variable genes, 13 PC = 9 clusters
table(seurat_campbell_refed@ident)##
## 0 1 2 3 4 5 6 7 8
## 732 385 334 304 112 100 96 75 36#######
seurat_campbell_refed <- FindClusters(object = seurat_campbell_refed, reduction.type = "pca", dims.use = 1:13,
resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_refed)## Parameters used in latest FindClusters calculation run on: 2018-11-21 00:44:18
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13seurat_campbell_refed <- RunTSNE(object = seurat_campbell_refed, dims.use = 1:13, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_refed)
TSNEPlot(object = seurat_campbell_refed,
no.legend = TRUE,
do.label = TRUE)
## 1451 variable genes, 20 PC = 11 clusters
table(seurat_campbell_refed@ident)##
## 0 1 2 3 4 5 6 7 8 9 10
## 376 374 370 324 233 111 100 95 79 76 36#######
seurat_campbell_refed <- FindClusters(object = seurat_campbell_refed, reduction.type = "pca", dims.use = 1:13,
resolution = 1.5, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_refed)## Parameters used in latest FindClusters calculation run on: 2018-11-21 00:44:18
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13seurat_campbell_refed <- RunTSNE(object = seurat_campbell_refed, dims.use = 1:13, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_refed)
TSNEPlot(object = seurat_campbell_refed,
no.legend = TRUE,
do.label = TRUE)
## 1451 variable genes, 20 PC = 14 clusters
table(seurat_campbell_refed@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13
## 351 335 263 224 219 206 111 100 80 77 77 76 36 19#######
seurat_campbell_refed <- FindClusters(object = seurat_campbell_refed, reduction.type = "pca", dims.use = 1:13,
resolution = 2.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
PrintFindClustersParams(object = seurat_campbell_refed)## Parameters used in latest FindClusters calculation run on: 2018-11-21 00:44:18
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function Algorithm n.start n.iter
## 1 1 100 10
## -----------------------------------------------------------------------------
## Reduction used k.param prune.SNN
## pca 30 0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10 11 12 13seurat_campbell_refed <- RunTSNE(object = seurat_campbell_refed, dims.use = 1:13, do.fast = TRUE)
# TSNEPlot(object = seurat_campbell_refed)
TSNEPlot(object = seurat_campbell_refed,
no.legend = TRUE,
do.label = TRUE)
## 1451 variable genes, 20 PC = 15 clusters
table(seurat_campbell_refed@ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 340 255 205 204 203 183 181 111 100 81 77 76 73 49 36## Cluster cells using final parameters (1451 genes, 13 PC, resolution = 1.0)
seurat_campbell_refed <- FindClusters(object = seurat_campbell_refed, reduction.type = "pca", dims.use = 1:13, resolution = 1.0, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
## Produce t-SNE of final parameters:
seurat_campbell_refed <- RunTSNE(object = seurat_campbell_refed, dims.use = 1:13, do.fast = TRUE)Use 1451 variable genes and 13 principal components with a resolution of 1.0. This gives a total of 11 clusters.
Finding differentially expressed genes between cell clusters (cluster biomarkers)
## Find markers for every cluster compared to all remaining cells, report both positive and negative genes.
seurat_campbell_refed_biomarkers <- FindAllMarkers(object = seurat_campbell_refed, only.pos = FALSE, min.pct = 0.2)
## Get the top 10 biomarkers per cluster
top10_seurat_campbell_markers<- seurat_campbell_refed_biomarkers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers## # A tibble: 110 x 7
## # Groups: cluster [11]
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 1.13e-283 2.92 0.995 0.152 2.17e-279 0 ENSMUSG00000005705
## 2 5.73e-242 3.36 0.992 0.282 1.10e-237 0 ENSMUSG00000029819
## 3 3.71e-211 1.88 0.657 0.042 7.14e-207 0 ENSMUSG00000026834
## 4 2.23e-187 2.04 0.71 0.084 4.29e-183 0 ENSMUSG00000021091
## 5 1.06e-132 1.44 0.819 0.231 2.04e-128 0 ENSMUSG00000051159
## 6 3.36e-108 1.19 0.455 0.049 6.47e-104 0 ENSMUSG00000021685
## 7 1.88e-100 1.17 0.803 0.284 3.62e- 96 0 ENSMUSG00000027523
## 8 6.34e- 92 1.10 0.434 0.058 1.22e- 87 0 ENSMUSG00000055737
## 9 3.46e- 86 1.17 0.622 0.169 6.67e- 82 0 ENSMUSG00000054667
## 10 4.68e- 81 1.23 0.726 0.272 9.02e- 77 0 ENSMUSG00000026360
## # ... with 100 more rowswrite.csv(as.data.frame(seurat_campbell_refed_biomarkers), file = "seurat_campbell_refed_biomarkers.csv", quote = FALSE)
write.csv(as.data.frame(top10_seurat_campbell_markers), file = "top10_seurat_campbell_refed_biomarkers.csv", quote = FALSE)
## Perform ROC DE test. This can take a long time.
seurat_campbell_refed_biomarkers_ROC <- FindAllMarkers(object = seurat_campbell_refed, only.pos = FALSE, min.pct = 0.2, test.use = "roc")
top10_seurat_campbell_markers_ROC<- seurat_campbell_refed_biomarkers_ROC %>% group_by(cluster) %>% top_n(10, avg_logFC)
top10_seurat_campbell_markers_ROC## # A tibble: 95 x 9
## # Groups: cluster [11]
## myAUC avg_diff power avg_logFC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0.983 3.36 0.966 3.36 0.992 0.282 NA 0 ENSMUSG00…
## 2 0.977 2.92 0.954 2.92 0.995 0.152 NA 0 ENSMUSG00…
## 3 0.836 1.44 0.672 1.44 0.819 0.231 NA 0 ENSMUSG00…
## 4 0.829 2.04 0.658 2.04 0.71 0.084 NA 0 ENSMUSG00…
## 5 0.814 1.88 0.628 1.88 0.657 0.042 NA 0 ENSMUSG00…
## 6 0.803 1.17 0.606 1.17 0.803 0.284 NA 0 ENSMUSG00…
## 7 0.766 1.23 0.532 1.23 0.726 0.272 NA 0 ENSMUSG00…
## 8 0.744 1.17 0.488 1.17 0.622 0.169 NA 0 ENSMUSG00…
## 9 0.722 1.10 0.444 1.10 0.62 0.231 NA 0 ENSMUSG00…
## 10 0.704 1.19 0.408 1.19 0.455 0.049 NA 0 ENSMUSG00…
## # ... with 85 more rowswrite.csv(as.data.frame(seurat_campbell_refed_biomarkers_ROC), file = "seurat_campbell_refed_biomarkers_ROC.csv", quote = FALSE)
write.csv(as.data.frame(top10_seurat_campbell_markers_ROC), file = "top10_seurat_campbell_refed_biomarkers_ROC.csv", quote = FALSE)
## Plot heatmap of top 10 DE genes
DoHeatmap(object = seurat_campbell_refed,
genes.use = top10_seurat_campbell_markers$gene,
slim.col.label = TRUE,
remove.key = TRUE)
Save seurat object of chow mice.
## save seurat object as .rds
saveRDS(seurat_campbell_refed, file = "./seurat_campbell_refed_final.rds")